Optimizing how we live, work, and play is embedded deep in the psyche of coders and American culture. But how much efficiency is too much?

Killer robots – they’re coming to get us! At least that’s what the majority of otherwise credible news sources would have us believe when tackling the very serious concerns around AI safety, as well as the increasingly clichéd use of photos of Terminator robots in such articles. It’s a pet peeve expressed by renowned AI researcher Eliezer Yudkowsky in a recent appearance on Sam Harris’ podcast, and a reference that betrays a deep misunderstanding of the ways in which our civilization’s future survival likely depends on ensuring the safe development of future artificial intelligence. For one thing, you’d think we’d be over ‘robots’. As was graphically illustrated by the apparent Russian meddling in the 2016 US presidential election, nefarious artificial intelligence is not some far-off thing – it’s already here and running wild throughout the World Wide Web in the form of bots. And yet somehow we still fail to recognize it for what it is, because our image of malicious artificial intelligence still looks like a gleaming metal endoskeleton with glowing red eyes and an evil grin.

Data Science can do good things for us: it improves life, it makes things more efficient, more effective and leads to a better experience. There are however some miss-steps that data-driven analysis has already exhibited. Here are few examples where data science tools were intentionally or unintentionally misused …

At a recent industry event, I was asked to be on a panel to discuss location-based marketing solutions. The group was in front of 100 local marketing professionals who specifically have the task of helping local businesses or national brands with a local retail presence. They drive more foot traffic to their bricks-and-mortar locations through various digital marketing efforts. This is the battleground for some of the most important data privacy questions of our time. There were three of us on stage, and honestly, this wasn’t one of your friendlier discussions. Because consumer location data is so helpful in targeting would-be visitors to a particular store location, this real-time data is intoxicating for most marketers. Imagine, every consumer walking down the street being bombarded with ads telling them where they can get lunch, a deal on coffee, or where to find the cheapest gas. Who doesn’t want that convenience and benefit? As it turns out, I don’t. And all the marketers on stage do.

Bias is a fundamental human characteristic. We are all biased, by our very nature, and every day we make countless decisions based on our gut feelings. We all have preconceived ideas, prejudices, and opinions. And that is fine, as long as we recognize it and take responsibility for it. The fundamental promise of AI, besides the dramatic increase of data processing power and business efficiency, is to help reduce the conscious or unconscious bias of human decisions. At the end of the day, this is what we expect from algorithms, isn’t it? Objectivity, mathematical detachment rather than fuzzy emotions, fact-based rather than instinctive decisions. Algorithms are supposed to alert people to their cognitive blind spots, so they can make more accurate, unbiased decisions. At least that’s the theory…

The Mission of The IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems To ensure every stakeholder involved in the design and development of autonomous and intelligent systems is educated, trained, and empowered to prioritize ethical considerations so that these technologies are advanced for the benefit of humanity. By ‘stakeholder’ we mean anyone involved in the research, design, manufacture, or messaging around intelligent and autonomous systems, including universities, organizations, governments, and corporations making these technologies a reality for society. Our goal is that Ethically Aligned Design will provide insights and recommendations that provide a key reference for the work of technologists in the related fields of science and technology in the coming years. To achieve this goal, in the current version of Ethically Aligned Design (EAD2v2), we identify pertinent ‘Issues’ and ‘Candidate Recommendations’ we hope will facilitate the emergence of national and global policies that align with these principles. The IEEE Global Initiative brings together several hundred participants from six continents, who are thought leaders from academia, industry, civil society, policy and government in the related technical and humanistic disciplines to identify and find consensus on timely issues. A second goal of The IEEE Global Initiative is to provide recommendations for IEEE Standards based on Ethically Aligned Design. Ethically Aligned Design (v1 and v2) and members of The IEEE Global Initiative are the inspiration behind the suite of IEEE P7000 Standards Working Groups that are free and open for anyone to join.
More information on other Working Groups:
IEEE P7000 – Model Process for Addressing Ethical Concerns During System Design
IEEE P7001 – Transparency of Autonomous Systems
IEEE P7002 – Data Privacy Process
IEEE P7003 – Algorithmic Bias Considerations
IEEE P7004 – Standard on Child and Student Data Governance
IEEE P7005 – Standard for Transparent Employer Data Governance
IEEE P7006 – Standard for Personal Data Artificial Intelligence (AI) Agent
IEEE P7007 – Ontological Standard for Ethically Driven Robotics and Automation Systems
IEEE P7008 – Standard for Ethically Driven Nudging for Robotic, Intelligent, and Automation Systems
IEEE P7009 – Standard for Fail-Safe Design of Autonomous and Semi-Autonomous Systems
IEEE P7010 – Wellbeing Metrics Standard for Ethical Artificial Intelligence and Autonomous Systems

Although facial recognition technology dates way back to the 1960s with the innovations of its founding father Woodrow Wilson Bledsoe, it was not until the last 10 years that it has truly come into its own. The newest solutions, including those created at Iflexion, are able to detect faces in a crowd with amazing accuracy. Due to this, they are effectively used in criminal identifications and can help in establishing the identity of missing people. However, such solutions also invoke a lot of criticism regarding the legality and ethics of their application. In this article, we’ll explore the issues that surround facial recognition in depth and look at how these technologies can be made safer for everyone.

Few days ago I attended a sharing session by an eminent professor and one thing captured my attention (which has already been mentioned as the topic’s name of this article). One interesting thing was that this topic during the sharing session turned out to be an endless (almost) question-answer-question kind of conversation between attendees and the professor. This made me think deeper and subsequently consolidated my thoughts to answer this question here from the perspective of business and technical standpoint.