There are many scientists and engineers who believe that computers will eventually become conscious. Computer scientists Bernard Baars and Stan Franklin wrote in 2009: ‘consciousness may be produced by … algorithms running on the machine.’ MIT Technology Review in October 2017 opined that this may happen in the not-too-distant future. Given the fact that biological evolution is slower than technological evolution, there is fear that humans will be unable to compete with sentient machines. No wonder many prominent voices in the scientific and the tech worlds are claiming that artificial intelligence is leading humanity to a catastrophe. Stephen Hawking told the BBC in 2014, ‘I think the development of full artificial intelligence could spell the end of the human race.’ Speaking to the National Governors Association in 2017, Tesla CEO Elon Musk said that ‘AI technology is a fundamental risk to the existence of human civilization.’

The story of how most tech companies generate profit has been a straightforward one: ‘users are unwaged labourers who produce goods (data and content) that are then taken and sold by the companies to advertisers and other interested parties’. In turn, this data is mainly fed to Artificial Intelligence (AI) systems that deliver services, improve production, drive innovation, etc. In fact, this economic model (digital economy) is probably the main source of innovation today, delivering massive ‘surplus to users and [being] ‘free’ (at point of use) to users’. Paradoxically, while these systems rely on the quantity and quality of data generated by humans they are also displacing workers at an unsettling rate; a recent study shows that AI could automate around 50% of jobs in 10 to 20 years. Another striking figure is that even though the combined revenues of Detroit’s ‘Big 3’ (GM, Ford and Chrystler) ‘were almost identical of to those of Silicon Villey’s ‘Big 3′ (Google, Apple, Facebook) in 2014, the latter had nine times fewer employees and worth thirty times more on the stock market’. This has prompted many economists to sound the alarms about the current distortions in market power, and call for new approaches. This article will attempt to summarise an alternative economic/social paradigm.

Technology is inherently about humans, and it is perilous to ignore social and psychological impact while creating tech. As engineers we must be aware of the unintended consequences of the technology we create.
With the advent of automotive AI and recent impact of social media platforms on elections, Ethics in AI has become one of the major areas of research.Few important (but not limited to) questions in Ethical AI are:
• Algorithmic Bias
• Autonomy & System Design
• Governance in AI
• Generative AI

One of our older stories is the demon summoning that goes bad. They always do. Our ancestors wanted to warn us: beware of beings who offer unlimited power. These days we depend on computational entities that presage the arrival of future demons – strong, human-level AIs. Luckily our sages, such as Nick Bostrom, are now thinking about better methods of control than the demon story tropes of appeasement, monkey cleverness, and stronger magical binding. After reviewing current thinking I’ll propose a protocol for safer access to AI superpowers.

In October 2017, there happened the uprising of an unprecedented online movement on social media by women across the world who started publicly sharing their untold stories of being sexually harassed along with the hashtag #MeToo (or some variants of it). Those stories did not only strike the silence that had long hid the perpetrators, but also allowed women to discharge some of their bottled-up grievances, and revealed many important information surrounding sexual harassment. In this paper, we present our analysis of about one million such tweets collected between October 15 and October 31, 2017 that reveals some interesting patterns and attributes of the people, place, emotions, actions, and reactions related to the tweeted stories. Based on our analysis, we also advance the discussion on the potential role of online social media in breaking the silence of women by factoring in the strengths and limitations of these platforms.

This chapter introduces readers to distinct Chinese, Japanese, and South Korean perspectives on and approaches to AI and robots as tools and partners in the AI ethics debate. Little discussed and often ignored, this sensitive topic commands our attention as it continues to grow in local importance. Given East Asia’s influential position as a source of global inspiration, development, and supply of AI and robotics, we would do well to inform ourselves of what’s to come. Each country’s perspectives on and approaches to AI and robots on the tool-partner spectrum are evaluated by examining its policy, academic thought, local practices, and popular culture. This analysis places South Korea in the tool range, China in the middle of the spectrum, and Japan in the partner range. All three countries hold a salient tension between top-down tool approaches and bottom-up partner perspectives. This tension is likely to increase both in magnitude and importance and shape local and global development and regulation trajectories in the years to come.

With the rapid development of artificial intelligence (AI), ethical issues surrounding AI have attracted increasing attention. In particular, autonomous vehicles may face moral dilemmas in accident scenarios, such as staying the course resulting in hurting pedestrians or swerving leading to hurting passengers. To investigate such ethical dilemmas, recent studies have adopted preference aggregation, in which each voter expresses her/his preferences over decisions for the possible ethical dilemma scenarios, and a centralized system aggregates these preferences to obtain the winning decision. Although a useful methodology for building ethical AI systems, such an approach can potentially violate the privacy of voters since moral preferences are sensitive information and their disclosure can be exploited by malicious parties. In this paper, we report a first-of-its-kind privacy-preserving crowd-guided AI decision-making approach in ethical dilemmas. We adopt the notion of differential privacy to quantify privacy and consider four granularities of privacy protection by taking voter-/record-level privacy protection and centralized/distributed perturbation into account, resulting in four approaches VLCP, RLCP, VLDP, and RLDP. Moreover, we propose different algorithms to achieve these privacy protection granularities, while retaining the accuracy of the learned moral preference model. Specifically, VLCP and RLCP are implemented with the data aggregator setting a universal privacy parameter and perturbing the averaged moral preference to protect the privacy of voters’ data. VLDP and RLDP are implemented in such a way that each voter perturbs her/his local moral preference with a personalized privacy parameter. Extensive experiments on both synthetic and real data demonstrate that the proposed approach can achieve high accuracy of preference aggregation while protecting individual voter’s privacy.

Data science and related fields, including artificial intelligence, business intelligence, and big data, are seeing tremendous growth. Data is important in numerous industries, from healthcare to transportation, making data scientists a must-have role in most companies. As more technology emerges, even more data can be collected, which only increases the need for experts.

General intelligence, the ability to solve arbitrary solvable problems, is supposed by many to be artificially constructible. Narrow intelligence, the ability to solve a given particularly difficult problem, has seen impressive recent development. Notable examples include self-driving cars, Go engines, image classifiers, and translators. Artificial General Intelligence (AGI) presents dangers that narrow intelligence does not: if something smarter than us across every domain were indifferent to our concerns, it would be an existential threat to humanity, just as we threaten many species despite no ill will. Even the theory of how to maintain the alignment of an AGI’s goals with our own has proven highly elusive. We present the first algorithm we are aware of for asymptotically unambitious AGI, where ‘unambitiousness’ includes not seeking arbitrary power. Thus, we identify an exception to the Instrumental Convergence Thesis, which is roughly that by default, an AGI would seek power, including over us.

In computer vision research, especially when novel applications of tools are developed, ethical implications around user perceptions of trust in the underlying technology should be considered and supported. Here, we describe an example of the incorporation of such considerations within the long-term care sector for tracking resident food and fluid intake. We highlight our recent user study conducted to develop a Goldilocks quality horizontal prototype designed to support trust cues in which perceived trust in our horizontal prototype was higher than the existing system in place. We discuss the importance and need for user engagement as part of ongoing computer vision-driven technology development and describe several important factors related to trust that are relevant to developing decision-making tools.

Recent progress in natural language generation has raised dual-use concerns. While applications like summarization and translation are positive, the underlying technology also might enable adversaries to generate neural fake news: targeted propaganda that closely mimics the style of real news. Modern computer security relies on careful threat modeling: identifying potential threats and vulnerabilities from an adversary’s point of view, and exploring potential mitigations to these threats. Likewise, developing robust defenses against neural fake news requires us first to carefully investigate and characterize the risks of these models. We thus present a model for controllable text generation called Grover. Given a headline like `Link Found Between Vaccines and Autism,’ Grover can generate the rest of the article; humans find these generations to be more trustworthy than human-written disinformation. Developing robust verification techniques against generators like Grover is critical. We find that best current discriminators can classify neural fake news from real, human-written, news with 73% accuracy, assuming access to a moderate level of training data. Counterintuitively, the best defense against Grover turns out to be Grover itself, with 92% accuracy, demonstrating the importance of public release of strong generators. We investigate these results further, showing that exposure bias — and sampling strategies that alleviate its effects — both leave artifacts that similar discriminators can pick up on. We conclude by discussing ethical issues regarding the technology, and plan to release Grover publicly, helping pave the way for better detection of neural fake news.

Ranked search results and recommendations have become the main mechanism by which we find content, products, places, and people online. With hiring, selecting, purchasing, and dating being increasingly mediated by algorithms, rankings may determine career and business opportunities, educational placement, access to benefits, and even social and reproductive success. It is therefore of societal and ethical importance to ask whether search results can demote, marginalize, or exclude individuals of unprivileged groups or promote products with undesired features. In this paper we present FairSearch, the first fair open source search API to provide fairness notions in ranked search results. We implement two algorithms from the fair ranking literature, namely FA*IR (Zehlike et al., 2017) and DELTR (Zehlike and Castillo, 2018) and provide them as stand-alone libraries in Python and Java. Additionally we implement interfaces to Elasticsearch for both algorithms, that use the aforementioned Java libraries and are then provided as Elasticsearch plugins. Elasticsearch is a well-known search engine API based on Apache Lucene. With our plugins we enable search engine developers who wish to ensure fair search results of different styles to easily integrate DELTR and FA*IR into their existing Elasticsearch environment.

The causes underlying unfair decision making are complex, being internalised in different ways by decision makers, other actors dealing with data and models, and ultimately by the individuals being affected by these decisions. One frequent manifestation of all these latent causes arises in the form of missing values: protected groups are more reluctant to give information that could be used against them, delicate information for some groups can be erased by human operators, or data acquisition may simply be less complete and systematic for minority groups. As a result, missing values and bias in data are two phenomena that are tightly coupled. However, most recent techniques, libraries and experimental results dealing with fairness in machine learning have simply ignored missing data. In this paper, we claim that fairness research should not miss the opportunity to deal properly with missing data. To support this claim, (1) we analyse the sources of missing data and bias, and we map the common causes, (2) we find that rows containing missing values are usually fairer than the rest, which should not be treated as the uncomfortable ugly data that different techniques and libraries get rid of at the first occasion, and (3) we study the trade-off between performance and fairness when the rows with missing values are used (either because the technique deals with them directly or by imputation methods). We end the paper with a series of recommended procedures about what to do with missing data when aiming for fair decision making.

Artificial Intelligence (AI) technology is rapidly changing many areas of society. While there is tremendous potential in this transition, there are several pitfalls as well. Using the history of computing and the world-wide web as a guide, in this article we identify those pitfalls and actions that lead AI development to its full potential. If done right, AI will be instrumental in achieving the goals we set for economy, society, and the world in general.

Understanding the difference is critical for understanding the future of work. AI and Cognitive Computing are often interchangeable terms to people who are not working in the technology industry. Both imply that computers are now responsible for performing job functions that a human used to perform. In fact, there’s a big difference between AI and Cognitive Computing. Understanding the difference will be intrinsic in facilitating the work of a person working in the intersection of these two.

It is standard in computational social choice to analyse welfare considerations under the assumptions of normalized utilities. In this note, we summarize some common reasons for this approach. We then mention another justification which is ignored but has solid normative appeal. The central concept used in the `new’ justification can also be used more widely as a social objective.

Prison labor’ is usually associated with physical work, but inmates at two prisons in Finland are doing a new type of labor: classifying data to train artificial intelligence algorithms for a startup. Though the startup in question, Vainu, sees the partnership as a kind of prison reform that teaches valuable skills, other experts say the claim of job training is more evidence of hype around the promises of AI. Vainu is building a comprehensive database of companies around the world that helps businesses find contractors to work with, says co-founder Tuomas Rasila. For this to work, people need to read through hundreds of thousands of business articles scraped from the internet and label whether, for example, an article is about Apple the tech company or a fruit company that has ‘apple’ in the name. (This labeled data is then used to train an algorithm that manages the database.)

Ensuring fairness and safety in artificial intelligence(AI) applications is considered by many the biggest challenge in the space. As AI systems match or surpass human intelligence in many areas, it is essential that we establish a guideline to align this new form of intelligence with human values. The challenge is that, as humans, we understand very little about how our values are represented in the brain or we can’t even formulate specific rules to describe a specific value. While AI operates in a data universe, human values are a byproduct of our evolution as social beings. We don’t describe human values like fairness or justice using neuroscientific terms but using arguments from social sciences like psychology, ethics or sociology Recently, researchers from OpenAI published a paper describing the importance of social sciences to improve the safety and fairness or AI algorithms in processes that require human intervention. We often hear that we need to avoid bias in AI algorithms by using fair and balanced training datasets. While that’s true in many scenarios, there are many instances in which fairness can’t be described using simple data rules. A simple question such as ‘do you prefer A to B’ can have many answers depending on the specific context, human rationality or emotion. Imagine the task of inferring a pattern of ‘happiness’, ‘responsibility’ or ‘loyalty’ given a specific dataset. Can we describe those values simply using data? Extrapolating that lesson to AI systems tells us that in order to align with human values we need help from the disciplines that better understand human behavior.

As the emerging field of predictive analytics in psychiatry generated and continues to generate massive interest overtime with its major promises to positively change and revolutionize clinical psychiatry, health care and medical professionals are greatly looking forward to its integration and application into psychiatry. However, by directly applying predictive analytics to the practice of psychiatry, this could cause detrimental damage to those that use predictive analytics through creating or worsening existing medical issues. In both cases, medical ethics issues arise, and need to be addressed. This paper will use literature to provide descriptions of selected stages in the treatment of mental disorders and phases in a predictive analytics project, approach mental disorder diagnoses using predictive models that rely on neural networks, analyze the complexities in clinical psychiatry, neural networks and predictive analytics, and conclude with emphasizing and elaborating on limitations and medical ethics issues of applying neural networks and predictive analytics to clinical psychiatry.

Design for ethics. Bake your values into your creations.

Part 1 explored the what and why of ethics of AI and divided the landscape into four realms – what AI is, what AI does, what AI impacts and what AI can be. In Part 2 I dive into the ethics of what AI is. The most commonly deployed form of AI can be described as a set of math functions (model) that given some inputs (data), learn *something* and use that to *infer* something else (make predictions). In other words AI is data, model and predictions. Ethical exploration of this realm covers issues like Bias in a models’ predictions and Fairness (or lack thereof) of the outcomes; as well as approaches to address them via Accountability and Transparency.

Ethics regarding social bias has recently thrown striking issues in natural language processing. Especially for gender-related topics, the need for a system that reduces the model bias has grown in areas such as image captioning, content recommendation, and automated employment. However, detection and evaluation of gender bias in the machine translation systems are not yet thoroughly investigated, for the task being cross-lingual and challenging to define. In this paper, we propose a scheme for making up a test set that evaluates the gender bias in a machine translation system, with Korean, a language with gender-neutral pronouns. Three word/phrase sets are primarily constructed, each incorporating positive/negative expressions or occupations; all the terms are gender-independent or at least not biased to one side severely. Then, additional sentence lists are constructed concerning formality of the pronouns and politeness of the sentences. With the generated sentence set of size 4,236 in total, we evaluate gender bias in conventional machine translation systems utilizing the proposed measure, which is termed here as translation gender bias index (TGBI). The corpus and the code for evaluation is available on-line.

On March 26, Google announced the formation of an external advisory group to help the company navigate complex questions around the ethical and responsible development of new technologies, including artificial intelligence. By April 4, however, the council had been disbanded, and Google acknowledged that the company was ‘going back to the drawing board.’ Ironically, Google’s new group of ethics advisors fell apart because of ethical challenges. But apart from underlining just how fragile the current state of technology ethics is, the incident attests to a much larger challenge tech companies are facing: How can a company ensure that the products it develops – especially A.I. – are as good for society as they are for the company’s bottom line? Google’s advisory council was established to help the company implement its A.I. principles – an ‘ethical charter to guide the responsible development and use of AI in our research and products.’ Launched last June, the principles articulate ideals and aspirations that few would dispute, including developing socially beneficial technologies, avoiding unfair bias, and ensuring safety. They mirror similar efforts from companies like Microsoft to develop an ethical foundation for A.I. development. They also reflect frameworks such as the Institute of Electrical and Electronics Engineers’ (IEEE) guidelines on ethically aligned design. At a time when there is legitimate growing concern over the potentially harmful personal and social impacts of A.I. and other technologies, these principles are laudable.

Comparing what has profoundly changed since before and after the Web 2.0, it’s very interesting – for not saying disappointing – to notice how we have shifted from a digital utopia where sharing, collaborating and free exchange reigned supreme, to an era where we start to doubt about the benevolence of computers and Internet. Web 2.0 Golden Age is indeed far behind us. While reading some articles, the word ‘algorithm’ seems to be used in the same way as if we were talking about a virus and at the other side, companies are entangled in a jungle of regulations and constraints. If we want to stand out the Before and After, we can only agree that social networks have totally disrupted the digital landscape – irreversibly. We don’t have to forget how and why Social Networks have been so popular since the day they came into our lives – People finally had the opportunity to be listened to. The tool is not criticizable in itself but what netizens do with it is far more worrying. A few years later the rise, we also have to accept that the best tool for promoting democracy is also the best way to spread misinformation and to manipulate public opinion.

Predicts the most common race of a surname based on U.S. Census data.

The paper describes how the new technologies and data they generate are transforming medicine. It stresses the uniqueness of heterogeneous medical data and the ways of dealing with them. It lists different sources that generate big medical data, their security, legal and ethical issues, as well as machine learning/AI methods of dealing with them. A unique feature of the paper is use of case studies to illustrate how the new technologies influence medical practice.

The new book by philosopher Deborah Mayo is relevant to data science for topical reasons, as she takes various controversial positions regarding hypothesis testing and statistical practice, and also as an entry point to thinking about the philosophy of statistics. The present article is a slightly expanded version of a series of informal reviews and comments on Mayo’s book. We hope this discussion will introduce people to Mayo’s ideas along with other perspectives on the topics she addresses.

Hiring robots for the workplaces is a challenging task as robots have to cater to customer demands, follow organizational protocols and behave with social etiquette. In this study, we propose to have a humanoid social robot, Nadine, as a customer service agent in an open social work environment. The objective of this study is to analyze the effects of humanoid robots on customers at work environment, and see if it can handle social scenarios. We propose to evaluate these objectives through two modes, namely, survey questionnaire and customer feedback. We also propose a novel approach to analyze customer feedback data (text) using sentic computing methods. Specifically, we employ aspect extraction and sentiment analysis to analyze the data. From our framework, we detect sentiment associated to the aspects that mainly concerned the customers during their interaction. This allows us to understand customers expectations and current limitations of robots as employees.

Data cooperatives with fiduciary obligations to members provide a promising direction for the empowerment of individuals through their own personal data. A data cooperative can manage, curate and protect access to the personal data of citizen members. Furthermore, the data cooperative can run internal analytics in order to obtain insights regarding the well-being of its members. Armed with these insights, the data cooperative would be in a good position to negotiate better services and discounts for its members. Credit Unions and similar institutions can provide a suitable realization of data cooperatives.

High-resolution individual geolocation data passively collected from mobile phones is increasingly sold in private markets and shared with researchers. This data poses significant security, privacy, and ethical risks: it’s been shown that users can be re-identified in such datasets, and its collection rarely involves their full consent or knowledge. This data is valuable to private firms (e.g. targeted marketing) but also presents clear value as a public good. Recent public interest research has demonstrated that high-resolution location data can more accurately measure segregation in cities and provide inexpensive transit modeling. But as data is aggregated to mitigate its re-identifiability risk, its value as a good diminishes. How do we rectify the clear security and safety risks of this data, its high market value, and its potential as a resource for public good? We extend the recently proposed concept of a tradeoff curve that illustrates the relationship between dataset utility and privacy. We then hypothesize how this tradeoff differs between private market use and its potential use for public good. We further provide real-world examples of how high resolution location data, aggregated to varying degrees of privacy protection, can be used in the public sphere and how it is currently used by private firms.

Over the past decade, artificial intelligence has demonstrated its efficiency in many different applications and a huge number of algorithms have become central and ubiquitous in our life. Their growing interest is essentially based on their capability to synthesize and process large amounts of data, and to help humans making decisions in a world of increasing complexity. Yet, the effectiveness of algorithms in bringing more and more relevant recommendations to humans may start to compete with human-alone decisions based on values other than pure efficacy. Here, we examine this tension in light of the emergence of several forms of digital normativity, and analyze how this normative role of AI may influence the ability of humans to remain subject of their life. The advent of AI technology imposes a need to achieve a balance between concrete material progress and progress of the mind to avoid any form of servitude. It has become essential that an ethical reflection accompany the current developments of intelligent algorithms beyond the sole question of their social acceptability. Such reflection should be anchored where AI technologies are being developed as well as in educational programs where their implications can be explained.

It is an emerging field of human computer interaction where we use natural language to exchange information and pass commands to computers. Any single interface with digital devices that you can think of can either be replaced or augmented with AI-enabled conversational interfaces. Examples include chatbots & speech based assistants like Siri. The modern smart city concept also involves a ‘citizen-centric’ services model which uses conversational AI interfaces to personalize and contextualize city services. One example is Citibot, a citizen engagement platform. Similarly, Vienna’s has a WienBot that allows residents and tourists to find common civic services like find parking , restrooms , Restaurants and other facilities. They no longer need to rely of kindness of strangers, or to scroll through long list on websites.

Andrew Zaldivar is a Developer Advocate for Google AI. His job is to help to bring the benefits of AI to everyone. Andrew develops, evaluates, and promotes tools and techniques that can help communities build responsible AI systems, writing posts for the Google Developers blog and speaking at a variety of conferences. Before joining Google AI, Andrew was a Senior Strategist in Google’s Trust & Safety group and worked on protecting the integrity of some of Google’s key products by using machine learning to scale, optimize and automate abuse-fighting efforts. Prior to joining Google, Andrew completed his Ph.D. in Cognitive Neuroscience from the University of California, Irvine and was an Insight Data Science fellow. Here, Andrew shares details on his role at Google, his personal and professional passions, and how he applies his academic background to his work creating and sharing tools that help teams build more inclusive products and user experiences.

We assert that it is the ethical duty of software engineers to strive to reduce software discrimination. This paper discusses how that might be done. This is an important topic since machine learning software is increasingly being used to make decisions that affect people’s lives. Potentially, the application of that software will result in fairer decisions because (unlike humans) machine learning software is not biased. However, recent results show that the software within many data mining packages exhibits ‘group discrimination’; i.e. their decisions are inappropriately affected by ‘protected attributes'(e.g., race, gender, age, etc.). There has been much prior work on validating the fairness of machine-learning models (by recognizing when such software discrimination exists). But after detection, comes mitigation. What steps can ethical software engineers take to reduce discrimination in the software they produce? This paper shows that making \textit{fairness} as a goal during hyperparameter optimization can (a) preserve the predictive power of a model learned from a data miner while also (b) generates fairer results. To the best of our knowledge, this is the first application of hyperparameter optimization as a tool for software engineers to generate fairer software.

The debate about the ethical implications of Artificial Intelligence dates from the 1960s. However, in recent years symbolic AI has been complemented and sometimes replaced by Neural Networks and Machine Learning techniques. This has vastly increased its potential utility and impact on society, with the consequence that the ethical debate has gone mainstream. Such debate has primarily focused on principles – the what of AI ethics – rather than on practices, the how. Awareness of the potential issues is increasing at a fast rate, but the AI community’s ability to take action to mitigate the associated risks is still at its infancy. Therefore, our intention in presenting this research is to contribute to closing the gap between principles and practices by constructing a typology that may help practically-minded developers apply ethics at each stage of the pipeline, and to signal to researchers where further work is needed. The focus is exclusively on Machine Learning, but it is hoped that the results of this research may be easily applicable to other branches of AI. The article outlines the research method for creating this typology, the initial findings, and provides a summary of future research needs.

We present contrastive fairness, a new direction in causal inference applied to algorithmic fairness. Earlier methods dealt with the ‘what if?’ question (counterfactual fairness, NeurIPS’17). We establish the theoretical and mathematical implications of the contrastive question ‘why this and not that?’ in context of algorithmic fairness in machine learning. This is essential to defend the fairness of algorithmic decisions in tasks where a person or sub-group of people is chosen over another (job recruitment, university admission, company layovers, etc). This development is also helpful to institutions to ensure or defend the fairness of their automated decision making processes. A test case of employee job location allocation is provided as an illustrative example.

When we program morality into robots, are we doomed to disappoint them with our very human ethical inconsistency?

When it comes to most things business, AI is making its mark as the must-have technology. Whether we are talking about customer-facing chatbots to help with engagement and conversion or AI working in the background to help make critical business decisions, AI is everywhere. And the expectations of what it can and should be able to do is often sky-high. When those expectations aren’t met, however, it’s not always the tech that’s to blame. More likely, it’s the humans who brought it on board. Here are some of the most common human errors when it comes to implementing AI.
Mistake #1: Confusing automation with AI
Mistake #2: Not determining success factors
Mistake #3: Not getting organizational buy-in
Mistake #4: Not considering the impact on the entire customer journey
Mistake #5: Not understanding the cause of the problems you’re trying to solve

Mission: ‘To catalyze and support research and initiatives for safeguarding life and developing optimistic visions of the future, including positive ways for humanity to steer its own course considering new technologies and challenges.’ We have technology to thank for all the ways in which today is better than the stone age, and technology is likely to keep improving at an accelerating pace. We are a charity and outreach organization working to ensure that tomorrow’s most powerful technologies are beneficial for humanity. With less powerful technologies such as fire, we learned to minimize risks largely by learning from mistakes. With more powerful technologies such as nuclear weapons, synthetic biology and future strong artificial intelligence, planning ahead is a better strategy than learning from mistakes, so we support research and other efforts aimed at avoiding problems in the first place. We are currently focusing on keeping artificial intelligence beneficial and we are also exploring ways of reducing risks from nuclear weapons and biotechnology. FLI is based in the Boston area, and welcomes the participation of scientists, students, philanthropists, and others nearby and around the world. Here is a video highlighting our activities from our first year.

Machine intelligence is here, and we’re already using it to make subjective decisions. But the complex way AI grows and improves makes it hard to understand and even harder to control. In this cautionary talk, techno-sociologist Zeynep Tufekci explains how intelligent machines can fail in ways that don’t fit human error patterns – and in ways we won’t expect or be prepared for. ‘We cannot outsource our responsibilities to machines,’ she says. ‘We must hold on ever tighter to human values and human ethics.’

How to cope with AI and start becoming a part of it If you open a news site today, you are almost sure to be met with an article about AI, Robotics, Quantum computing, genetic engineering, autonomous vehicles, natural language processing, and other technologies from the box called ‘The fourth industrial revolution’. Rating these technologies makes no sense, as they all have a staggering potential to change our world forever. Artificial intelligence, however, is already surging into all of the other technologies. To facilitate by the mastering of big data, pattern recognition, or prediction is an inherent quality of AI, and is frequently being applied to support ground-breaking discoveries in other technologies. I once heard a driving instructor comparing holding the hands on the steering wheel to having a gun in the hand, because of how dangerous it is to drive a car. AI is also dangerous, and we need to face the dark side of AI too, not only revel in the glorious benefits it brings us. Anything else would be reckless driving.

AI ‘thinks’ like those who designed them – with a heteronormative conception of gender. They exclude transgender people and reinforce gender stereotypes. Worse, governments across the world spend billions of dollars to scale cis-sexist AI to new industries like government agencies and to new applications like image recognition with little regard for their gendered impacts. The computer science community, tech community, and government agencies should be more accountable for the gendered impacts of their algorithms. They need to learn to analyze the gendered impacts of algorithms using queer and transgender theory then apply that learning to the design, deployment, and monitoring of AI algorithms in society.

Few current laws address the use of artificial intelligence. That puts companies under greater pressure to reassure the public that their AI applications are ethical and fair.

Statisticians feel recently a pressure for substituting sample surveys with new opportunities offered by Big Data. Some authors suggest that opinion polls and other random sample surveys have become obsolete in the new era of Big Data. The author discusses relationships between survey-based and Big Data-based approaches to the measurement of consumers’ and public opinions. Special attention is given to traditional opinion polls.

A machine learning algorithm. OpenAI’s GPT2 language model is trained to predict text. It’s huge, complex, takes months of training over tons of data on expensive computers; but once that’s done it’s easy to use. A prompt (‘The Hitchhiker’s Guide to AI Ethics is a’) and a little curation is all it took to generate my raving review using a smaller version of GPT2. The text has some obvious errors but it is a window into the future. If AI can generate human-like output can it also make human-like decisions? Spoiler alert: yes it can, it already is. But is *human-like* good enough? What happens to TRUST in a world where machines generate human-like output and make human-like decisions? Can I trust an autonomous vehicle to have seen me? Can I trust the algorithm processing my housing loan to be fair? Can we trust the AI in the ER enough to make life and death decisions for us? As technologists we must flip this around and ask: How can we make algorithmic systems trustworthy? Enter Ethics. To understand more we need some definitions, a framework, and lots of examples. Let’s go!

A close look at how AI & algorithms reinforce prejudices and biases of its human creators and societies, and how to fight discrimination.

If you experience something – record it. If you record something – upload it. If you upload something – share it’ – this phrase best explains dataism, the new 21st-century religion focused mainly on the rapid development of technology, Internet obsession, and general data-worship. Artificial Intelligence, Machine Learning, Data Science, Big Data.. these things and more are so powerful and new for the majority of us that we start to fear what kind of dangerous transformations it may bring. Science fiction, however, is quickly becoming science fact – the future is the machine. But, isn’t it too early for assuming something like that and declare technology will destroy humanity? The truth is, right now Dataism is far away from being a pure religion in its meaning, or scientific concept grounding on true laws. It is rather a complex of fear and a vision that AI and other stuff are nothing more than just manipulation and threat. Weird comparison, but just like capitalism, Dataism too began as a neutral scientific theory but is now mutating into a religion that claims to determine right and wrong. So, to believe in Dataism or not? Let’s make a little investigation on this.

The integration of Artificial Intelligence (AI) into weapon systems is one of the most consequential tactical and strategic decisions in the history of warfare. Current AI development is a remarkable combination of accelerating capability, hidden decision mechanisms, and decreasing costs. Implementation of these systems is in its infancy and exists on a spectrum from resilient and flexible to simplistic and brittle. Resilient systems should be able to effectively handle the complexities of a high-dimensional battlespace. Simplistic AI implementations could be manipulated by an adversarial AI that identifies and exploits their weaknesses. In this paper, we present a framework for understanding the development of dynamic AI/ML systems that interactively and continuously adapt to their user’s needs. We explore the implications of increasingly capable AI in the kill chain and how this will lead inevitably to a fully automated, always on system, barring regulation by treaty. We examine the potential of total integration of cyber and physical security and how this likelihood must inform the development of AI-enabled systems with respect to the ‘fog of war’, human morals, and ethics.

Tai, a senior at the University of Pennsylvania, wakes up at the perfect time every morning – well-rested, but not late for classes or appointments. Today that meant rising at 7:18 a.m. He did not set his alarm for that time. Rather, it was chosen for him. His phone’s sleep-tracker app had been following his sleep patterns over the past few months, monitoring his REM cycles and periods of lighter rest. Using this information, it set a smart alarm that wakes him during a light stage of sleep, while also trying to maintain some level of consistency over time. The theory is that this schedule will prime Tai for greater energy and concentration throughout the day. Tai needs to be sharp. He’s at a turning point in his life, about to step away from the relatively safe world of college – of information-gathering, homework, and exams – into the ‘real’ world of practical problem solving: finding a job, choosing a place to live, and negotiating the wonderful but complicated details of a romantic relationship that’s getting more serious by the day.

AI and People Analytics have taken off. As I’ve written about in the past, the workplace has become a highly instrumented place. Companies use surveys and feedback tools to get our opinions, new tools monitor emails and our network of communications (ONA), we capture data on travel, location, and mobility, and organizations now have data on our wellbeing, fitness, and health. And added to this is a new stream of data which includes video (every video conference can be recorded and more than 40% of job interviews are recorded), audio (tools that record meetings can sense mood), and image recognition that recognizes faces wherever we are.

Machine learning is gradient-descending its way into more and more places, and with its arrival comes both increasing demand for skilled ML practitioners and increasingly disruptive challenges to the basic assumptions we hold about our society. The truth is, ML is fast becoming too deeply integrated into our world to keep engineering and ethical concerns separate from each other, and to survive the wave it’s creating we’re going to need a workforce that is acutely aware of how its work ripples through the surrounding world. Where better to start increasing this awareness than the classroom?

Human robots will take your job before AI. The human robot is you, and you will help AI steal your job tomorrow. Will you become ‘useless’?

Wide-ranging applications of data science bring utopian proposals of a world free from bias, but in reality, machine learning models reproduce the inequalities that shape the data they’re fed. Can programmers free their models from prejudice?

The aim of the NumJobs project is to provide to firms and organizations an innovative tool to simulate the impact of digital and AI technologies on economies and societies. Digitalization, automation, robotization, rise of artificial intelligence (AI), as many movements and factors which constitute the digital revolution, often founded on innovations of rupture.The bigger issue (or fear depending on point of view) here concerning these digital innovations is that of the impact on employment. Are they sources of job creation, or will they involve destruction – possibly massive – of employment ? If there is job creation, is it through new types of occupation ? And which actual professions would be threatened of extinction? Moreover, beyond the question of creation or destruction, how this digital revolution will modify the nature of work, the actual tasks, the work conditions, the place of work in the life of the individuals and the society ?Thus, the impact of digitalization about employment not only concerns the economy but also the whole society. Innovations lead destructions of existing employment and creations of new jobs. Among them, disruptive innovations create new sectors, and could trigger waves of innovations that are potentially engines of wealth creation by diffusion of purchasing power to the other sectors of the economy. They have however destroying effects on certain types of jobs.Previous industrial revolutions have not induced a net negative effect on the number of jobs. Productivity and new products have raised incomes and demand for products. The key issue we aim to tackle is whether the digital technology has similar effects or may involve negative effects on the number of jobs and their structure.

This Guidebook will help you build human-centered AI products. It’ll enable you to avoid common mistakes, design excellent experiences, and focus on people as you build AI-driven applications. It was written for user experience (UX) professionals and product managers as a way to help create a human-centered approach to AI on their product teams. However, this Guidebook should be useful to anyone in any role wanting to build AI products in a more human-centered way.

Algorithms determine the people we meet on Tinder, recognize your face to open the keyless door or fire you when your productivity drops. Machines are used to make decisions about health, employment, education, vital financial and criminal sentencing. Algorithms are used to decide, who gets a job interview, who gets a donor organ or how an autonomous car reacts in a dangerous situation.

The EU has issued a set of guidelines, ‘Ethics Guidelines for Trustworthy AI’ to help tech companies steer towards ethical and inclusive AI as we come to terms with the potential of this technology.

Not too long ago we discussed the AI Apocalypse as it pertained to the Facebook #TenYearChallenge. Is Facebook evil? Are we evil for helping usher in our own demise? As we put it: not quite. However, AI & ethics seem inexorably linked and for good reason. This is part of an ongoing series on the question of AI and ethics. And there’s no better place to start than with science fiction, of course. The question of what artificial intelligence could be capable of has captured our imaginations for a long while. The truth is, the idea may stretch as far back, at least in concept, to Ancient Greece. To get philosophical, the idea of what humankind’s creations could be capable of is not new. Neither is the notion of how we would contend with this. However, at least in a modern sense, Isaac Asimov was instrumental in thrusting the question into the public debate. At least as it pertains to artificial intelligence. Namely, robots. What could robots do? And how could we stop them?

The Center for Data Innovation is the leading think tank studying the intersection of data, technology, and public policy. With staff in Washington, D.C., and Brussels, the Center formulates and promotes pragmatic public policies designed to maximize the benefits of data-driven innovation in the public and private sectors. It educates policymakers and the public about the opportunities and challenges associated with data, as well as important data-related technology trends. The Center is a non-profit, non-partisan research institute affiliated with the Information Technology and Innovation Foundation. Robust technical standards for AI will be crucial to the success of the technology in the United States and abroad because they can serve as authoritative guidelines and benchmarks for the development and evaluation of AI. However thus far, concerns about the oversight of AI have stymied productive discussions about standards development by causing policymakers to prioritize oversight at the expense of technical understanding. NIST should shift this focus back to technical standards development to provide a sound scientific underpinning for any future efforts to increase oversight of AI. Additionally, NIST should strengthen U.S. leadership in developing AI standards and encouraging their broad adoption to ensure a globally competitive marketplace.

The U.S. Department of Commerce’s National Institute of Standards and Technology (NIST) is seeking information about technical standards and related tools for artificial intelligence (AI). The Request for Information (RFI), published today in the Federal Register, is in response to the Feb. 11, 2019, Executive Order on Maintaining American Leadership in Artificial Intelligence. The executive order directs NIST to create a plan for federal engagement in the development of these standards and tools in support of reliable, robust and trustworthy systems that use AI technologies. ‘The inputs of the U.S. stakeholder community are essential to inform development of a plan that will support continued American leadership in AI,’ said Under Secretary of Commerce for Standards and Technology and NIST Director Walter G. Copan. ‘Sound technical standards, performance metrics and tools are needed to foster public trust and confidence in AI technologies, enabling the market adoption of the next wave of innovations that will contribute to the economic and national security of the United States.’ To develop the plan, NIST will engage with other federal agencies, the private sector, academic institutions, nongovernmental organizations and other stakeholders with an interest and expertise in AI and related standards.

The February 11, 2019, Executive Order on Maintaining American Leadership in Artificial Intelligence (AI) directs the National Institute of Standards and Technology (NIST) to create a plan for Federal engagement in the development of technical standards and related tools in support of reliable, robust, and trustworthy systems that use AI technologies (Plan). This notice requests information to help NIST understand the current state, plans, challenges, and opportunities regarding the development and availability of AI technical standards and related tools, as well as priority areas for federal involvement in AI standards-related activities. To assist in developing the Plan, NIST will consult with Federal agencies, the private sector, academia, non-governmental entities, and other stakeholders with interest in and expertise relating to AI.

The technology behind mobile banking is pretty incredible, but what happens when there’s a mistake? What happens if we don’t see the mistake? We’re living in a world where so many technological advancements have been made that they almost blend into the background. We’ve gotten used to the idea that we can let our phones and computers do the little things for us. It’s easy to forget how new all of this technology really is. But it is new. It’s changing every day. There are algorithms behind most of the basic things that you take for granted, from social media and entertainment to banking and finances. They are constantly evolving. They are not perfect.

Omdena and Ciencia y Datos has partnered up for a new AI Challenge, if you want to be one of the 50 AI enthusiasts to acquire hands-on skills by solving a meaningful and global problem you need to read this article.

In the last decade, the area of artificial intelligence (AI) has exploded with interesting and promising results. With major achievements in image recognition, speech recognition and highly complex games, AI continues to disrupt society. This blog post will discuss practical applications of AI, optimization and interpretability of deep learning models and reinforcement learning (RL), based on the 2018 REWORK Deep Learning Summit in Toronto. Four software engineers from Knowit had the pleasure of travelling to Canada to attend this conference, and with renowned speakers such as Geoff Hinton attending, it turned out be an insightful experience.

Although algorithms will get better at advising on the most difficult of judgement calls, we need to remain realistic about where the boundaries currently lie and accept that humans still have a critical role to play in decision-making.

Fairness has become a central issue for our research community as classification algorithms are adopted in societally critical domains such as recidivism prediction and loan approval. In this work, we consider the potential bias based on protected attributes (e.g., race and gender), and tackle this problem by learning latent representations of individuals that are statistically indistinguishable between protected groups while sufficiently preserving other information for classification. To do that, we develop a minimax adversarial framework with a generator to capture the data distribution and generate latent representations, and a critic to ensure that the distributions across different protected groups are similar. Our framework provides a theoretical guarantee with respect to statistical parity and individual fairness. Empirical results on four real-world datasets also show that the learned representation can effectively be used for classification tasks such as credit risk prediction while obstructing information related to protected groups, especially when removing protected attributes is not sufficient for fair classification.

Now that machine learning algorithms lie at the center of many resource allocation pipelines, computer scientists have been unwittingly cast as partial social planners. Given this state of affairs, important questions follow. What is the relationship between fairness as defined by computer scientists and notions of social welfare? In this paper, we present a welfare-based analysis of classification and fairness regimes. We translate a loss minimization program into a social welfare maximization problem with a set of implied welfare weights on individuals and groups–weights that can be analyzed from a distribution justice lens. In the converse direction, we ask what the space of possible labelings is for a given dataset and hypothesis class. We provide an algorithm that answers this question with respect to linear hyperplanes in $\mathbb{R}^d$ that runs in $O(n^dd)$. Our main findings on the relationship between fairness criteria and welfare center on sensitivity analyses of fairness-constrained empirical risk minimization programs. We characterize the ranges of $\Delta \epsilon$ perturbations to a fairness parameter $\epsilon$ that yield better, worse, and neutral outcomes in utility for individuals and by extension, groups. We show that applying more strict fairness criteria that are codified as parity constraints, can worsen welfare outcomes for both groups. More generally, always preferring ‘more fair’ classifiers does not abide by the Pareto Principle—a fundamental axiom of social choice theory and welfare economics. Recent work in machine learning has rallied around these notions of fairness as critical to ensuring that algorithmic systems do not have disparate negative impact on disadvantaged social groups. By showing that these constraints often fail to translate into improved outcomes for these groups, we cast doubt on their effectiveness as a means to ensure justice.

Ethics is not a new area of focus for large organizations, so perhaps it’s no surprise that these respondents indicated that they are preparing their employees for the ethical deployment of AI. What sets AI apart, however, is the recognition that an artificially intelligent process is one for which the organization has responsibility for the outcomes – in the same way that the organization is responsible for the actions of its employees. Ethical training for technologists signals an understanding of the stakes involved with potentially unethical uses of AI. AI adopters indicated relatively strong ethical processes in place today, with 63 percent affirming that they ‘have an ethics committee that reviews the use of AI,’ and 70 percent indicating they ‘conduct ethics training for their technologists.’

Recently, policymakers, regulators, and advocates have raised awareness about the ethical, policy, and legal challenges posed by machine learning and data-driven systems. In particular, they have expressed concerns about their potentially discriminatory impact, for example, due to inadvertent encoding of bias into automated decisions. For search and recommendation systems, our goal is to understand whether there is bias in the underlying machine learning models, and devise techniques to mitigate the bias. This paper presents a framework for quantifying and mitigating algorithmic bias in mechanisms designed for ranking individuals, typically used as part of web-scale search and recommendation systems. We first propose complementary measures to quantify bias with respect to protected attributes such as gender and age. We then present algorithms for computing fairness-aware re-ranking of results towards mitigating algorithmic bias. Our algorithms seek to achieve a desired distribution of top ranked results with respect to one or more protected attributes. We show that such a framework can be utilized to achieve fairness criteria such as equality of opportunity and demographic parity depending on the choice of the desired distribution. We evaluate the proposed algorithms via extensive simulations and study the effect of fairness-aware ranking on both bias and utility measures. We finally present the online A/B testing results from applying our framework towards representative ranking in LinkedIn Talent Search. Our approach resulted in tremendous improvement in the fairness metrics without affecting the business metrics, which paved the way for deployment to 100% of LinkedIn Recruiter users worldwide. Ours is the first large-scale deployed framework for ensuring fairness in the hiring domain, with the potential positive impact for more than 575M LinkedIn members.

Progress on the UN Sustainable Development Goals (SDGs) is hampered by a persistent lack of data regarding key social, environmental, and economic indicators, particularly in developing countries. For example, data on poverty — the first of seventeen SDGs — is both spatially sparse and infrequently collected in Sub-Saharan Africa due to the high cost of surveys. Here we propose a novel method for estimating socioeconomic indicators using open-source, geolocated textual information from Wikipedia articles. We demonstrate that modern NLP techniques can be used to predict community-level asset wealth and education outcomes using nearby geolocated Wikipedia articles. When paired with nightlights satellite imagery, our method outperforms all previously published benchmarks for this prediction task, indicating the potential of Wikipedia to inform both research in the social sciences and future policy decisions.

First, as Yogi Berra said, ‘It’s tough to make predictions, especially about the future.’ Where is my flying car? Second, the title reads like clickbait, but surprisingly it appears to be pretty close to the actual survey, which asked AI researchers when ‘high-level machine intelligence’ will arrive, defined as ‘when unaided machines can accomplish every task better and more cheaply than human workers.’ What is a ‘task’ in this definition? Does ‘every task’ even make sense? Can we enumerate all tasks? Third and most important, is high-level intelligence just accomplishing tasks? This is the real difference between artificial and human intelligence: humans define goals, AI tries to achieve them. Is the hammer going to displace the carpenter? They each have a purpose. This difference between artificial and human intelligence is crucial to understand, both to interpret all the crazy headlines in the popular press, and more importantly, to make practical, informed judgements about the technology. The rest of this post walks through some types of artificial intelligence, types of human intelligence, and given how different they are, plausible and implausible risks of artificial intelligence. The short story: unlike humans, every AI technology has a perfectly mathematically well-defined goal, often a labeled dataset.

Autonomous mechanisms have been proposed to regulate certain aspects of society and are already being used to regulate business organisations. We take seriously recent proposals for algorithmic regulation of society, and we identify the existing technologies that can be used to implement them, most of them originally introduced in business contexts. We build on the notion of ‘social machine’ and we connect it to various ongoing trends and ideas, including crowdsourced task-work, social compiler, mechanism design, reputation management systems, and social scoring. After showing how all the building blocks of algorithmic regulation are already well in place, we discuss possible implications for human autonomy and social order. The main contribution of this paper is to identify convergent social and technical trends that are leading towards social regulation by algorithms, and to discuss the possible social, political, and ethical consequences of taking this path.

I shall not today attempt further to define the kinds of material … within that shorthand description … but I know it when I see it.’ This rationale is used to categorize the struggle to define something that is hard to define, like taste, art, beauty or, in this case, obscenity, as famously stated by U.S. Supreme Court Justice Potter Stewart. That perspective could easily be applied to digital ethics today. Gartner defines digital ethics as the systems of values and moral principles for the conduct of electronic interactions. But what does this really mean? Everyone agrees we need to decide what is ethical and what is not, yet most executives and organizations seem to operate on an ‘I know it when I see it’ basis.

The Artificial Intelligence paradigm (hereinafter referred to as ‘AI’) builds on the analysis of data able, among other things, to snap pictures of the individuals’ behaviors and preferences. Such data represent the most valuable currency in the digital ecosystem, where their value derives from their being a fundamental asset in order to train machines with a view to developing AI applications. In this environment, online providers attract users by offering them services for free and getting in exchange data generated right through the usage of such services. This swap, characterized by an implicit nature, constitutes the focus of the present paper, in the light of the disequilibria, as well as market failures, that it may bring about. We use mobile apps and the related permission system as an ideal environment to explore, via econometric tools, those issues. The results, stemming from a dataset of over one million observations, show that both buyers and sellers are aware that access to digital services implicitly implies an exchange of data, although this does not have a considerable impact neither on the level of downloads (demand), nor on the level of the prices (supply). In other words, the implicit nature of this exchange does not allow market indicators to work efficiently. We conclude that current policies (e.g. transparency rules) may be inherently biased and we put forward suggestions for a new approach.

The emergence of artificial intelligence (AI) and its progressively wider impact on many sectors across the society requires an assessment of its effect on sustainable development. Here we analyze published evidence of positive or negative impacts of AI on the achievement of each of the 17 goals and 169 targets of the 2030 Agenda for Sustainable Development. We find that AI can support the achievement of 128 targets across all SDGs, but it may also inhibit 58 targets. Notably, AI enables new technologies that improve efficiency and productivity, but it may also lead to increased inequalities among and within countries, thus hindering the achievement of the 2030 Agenda. The fast development of AI needs to be supported by appropriate policy and regulation. Otherwise, it would lead to gaps in transparency, accountability, safety and ethical standards of AI-based technology, which could be detrimental towards the development and sustainable use of AI. Finally, there is a lack of research assessing the medium- and long-term impacts of AI. It is therefore essential to reinforce the global debate regarding the use of AI and to develop the necessary regulatory insight and oversight for AI-based technologies.

Die ‘Hochrangige Expertengruppe zur KI’ (AI HLG) hat Anfang April ihre Richtlinien für eine vertrauenswürdige KI vorgelegt. Die EU-Kommission hatte das Gremium im Sommer vergangenen Jahres beauftragt. Auf Grundlage von vier ethischen Grundsätzen für die Entwicklung und Anwendung von KI-Systemen formulierten die 52 Kommissionsmitglieder in dem Papier sieben Anforderungen für KI-Unternehmen und deren Entwickler. Grundsätze sind die Anerkennung der Handlungsautonomie des einzelnen und des Prinzips ‘do no harm’, also keinen Schaden anzurichten und niemanden zu verletzen, sowie Fairness und Nachvollziehbarkeit. Zu den sieben Anforderungen, auf die KI-Entwicklungen abzuklopfen seien, gehören technische Robustheit und Sicherheit, Vertraulichkeit und klare Datenhaltungsregeln, Transparenz, Nichtdiskriminierung, Berücksichtigung gesamtgesellschaftlicher Effekte und Maßnahmen zur Rechenschaftslegung. Über allem steht die Autonomie und Kontrolle für den einzelnen. Wie gut sich diese Regeln in der Entwicklung und in Anwendungen einbauen lassen, sollen Unternehmen ab dem Sommer bis Ende 2019 testen. Die Debatte über eine ethische KI flankiert ein mit den Mitgliedsstaaten abgestimmtes großes Investitionsprogramm: 20 Milliarden Euro sind bis Ende 2020 vorgesehen, davon 1,5 Milliarden EU-Gelder. Damit möchte man die vertrauenswürdige ‘KI made in Europe’ gerne in dem von China und den USA dominierten Markt positionieren.

With the growing popularity of computer vision and facial recognition, businesses strive to adopt innovations to keep heads above water. As Accenture’s data scientists report, in such areas as security, customer interactions, retail, marketing, etc., they predict the future full of tech invasion, no less. In this future, different scenarios are possible. It spurs inevitable discussions. That way it was with Amazon’s Recognition when a group of stakeholders expressed worries about potential abuses of the facial recognition technology for surveillance purposes. And not in vain. Commonly, the main argument for implementing AI-based innovations, e.g., facial recognition, is the intention to increase the ROI. This situation can’t but raise ethical questions. How should organizations tackle the downsides of AI? It’s time to figure things out.

A few years ago, I had a conversation with a person who had opinions of what it means to be an American. We were discussing the Japanese Internment during World War II. As an Asian American, I feared that rising racial tensions and conflict with Asian countries might lead to another round of internment of Asian Americans. That person stated that the sheer number of Asian Americans make it impossible to intern them now. Then, this person stated that ‘internment’ is a primitive method of controlling a group of people. In the age of Artificial Intelligence, augmented reality will be able to control people’s lives by altering every aspect of those people’s lives. In the name of national security, in times of world conflict, a group of people can be persecuted by technology without even knowing that they are persecuted.

Last year, my colleague J. P. Gownder and I got to talking about the impact on employees both from companies’ automation efforts and their eagerness to integrate artificial intelligence and robots. We noticed that most of the predictions of how this would go were either utopias viewed through rose-colored glasses or dystopian nightmares darker than most science-fiction novels. Perfect or perfectly terrible future scenarios both sounded unlikely to us. More importantly, we believed that companies needed a plan for creating future employee experiences that didn’t leave humans either out of work or with jobs that left little for them to do. And so we recently published a new report to help companies, ‘Start Designing The Future Human-Machine Workplace Now.’ The goal for every company should be to ensure that their humans can thrive when they work alongside robots and AI. To do that, we lay out three principles for creating future employee experiences.