How can businesses protect sensitive user data to foster trust among consumers? How can they design products and tools that avoid racial, gender, and other biases towards users? How should they decide who uses their products, and for what purpose?
The answers to these and other questions are especially pertinent to the developers of artificial intelligence. And they require the input and expertise of not just computer scientists and engineers, but also lawyers, philosophers, business leaders, and regulators. Fostering productive and ongoing dialogue between these groups is essential.
The Digital Initiative at 性视界 Business School provided one such forum at the recent Digital Transformation Summit: AI, Ethics, and Business Decisions. Speakers discussed the challenges of integrating ethics into routine decision making, how law and policy will have to adapt to the changing realities of AI, and how some pioneering companies are navigating this uncertain environment.
It鈥檚 no secret that AI has come under public scrutiny in recent years. From the role of Facebook algorithms in escalating , to the , it鈥檚 clear that the potential applications鈥攁nd hazards鈥攐f AI are both serious and sometimes difficult to predict. According to Dr. Cansu Canca, founder and director of the AI Ethics Lab, companies must work proactively to identify, understand, analyze, and implement solutions to ethical concerns surrounding AI at every stage of the product design and development process.
To do this, Canca says, 鈥渨e need to train researchers and developers to engage in this kind of structured thinking when they are dealing with ethics questions鈥� so that they can identify these problems early on as they arise and tackle them in real time, at each stage of the innovation process.鈥� This approach aims to avoid unethical outcomes while enhancing the technology and avoiding costly redesigns and product delays. Perhaps most importantly, Canca explains, it is a proactive rather than reactive system鈥攐utlining a framework for researchers to take forward as they confront the multi-faceted and evolving challenges posed by AI technology, rather than responding on a case-by-case basis.
Just as companies must adapt to the ethical issues posed by AI, so too does our legal system. If an Uber AV crashes into a human-operated vehicle making a left turn, who is to blame? Does your answer change if you know that the AV had the right of way, but the driver鈥檚 situational knowledge informed her decision to make the turn? According to Matthew Wansley, general counsel at nuTonomy, 鈥渏urors are going to increasingly find that moral intuition cannot generate a determinate answer鈥� in these kinds of situations. In other words, it鈥檚 easy to assess the liability of a drunk or otherwise impaired driver鈥攍ess so for an AV whose only failing might be imperfect or incomplete coding. So how can, or should, the legal system respond? 鈥淭here is no obvious and easy solution,鈥� Wansley says. Wansley suggests that tort law will need to evolve, possibly by relying less on lay juries to adjudicate liability.
The legal gray area surrounding AI is part of the reason that so much responsibility for safety and accountability currently falls to the companies at the forefront of this technology. Dr. Rana el Kaliouby, CEO and co-founder of , is taking this responsibility very seriously, and she is on a mission to popularize what she terms 鈥渁rtificial emotional intelligence鈥� or 鈥淓motion AI.鈥� Why is this so important? As el Kaliouby explains, AI is becoming increasingly present in our daily lives, and whether we realize it or not, 鈥渨e are forming a new kind of partnership between humans and machines.鈥� 鈥淭his partnership,鈥� she asserts, 鈥渞equires a new social contract based on mutual trust.鈥�
El Kaliouby believes part of the reason AI has experienced so many high-profile failures is that while the technology has a very high IQ, it possesses no empathy or emotional intelligence. 鈥淭hat鈥檚 the missing link,鈥� she says鈥攅motionally intelligent technology that can read the nuance of human facial expressions and emotional cues to achieve a deeper understanding of its user or operator. At Affectiva, el Kaliouby and her team have collected and analyzed data from over 7.8 million faces from 87 countries in order to develop software that can do just this. Such a large and diverse data set is essential to teach machines to read the incredibly varied and nuanced spectrum of human states in order to avoid algorithmic bias.
But development is only one part of this social contract. With a technology so ubiquitous as AI, the use cases are seemingly endless鈥攈owever they are not all equal. As el Kaliouby explains, 鈥渢echnology is neutral.” The same software that can help improve automotive safety and make advances in mental health could also potentially be used to manipulate and discriminate against users.
That鈥檚 why, el Kaliouby recounts, Affectiva turned down a $40 million investment from a security agency in 2011. 鈥淚 asked myself,鈥� she said, 鈥渄o I want to spend my mindshare, and my team鈥檚 mindshare, on a problem where we鈥檙e not building trust and respecting users?鈥� The company鈥攁s a team鈥攄ecided that the answer was no. A commitment to inclusion and value-based decision making is key for companies faced with the decision of how to apply their technology, and who to partner with. Indeed, in recent years, many companies鈥攊ncluding Google, Microsoft, and others鈥攈ave weathered employee protests over the licensing of products to security and surveillance agencies.
So although the path ahead for AI is far from clear, what鈥檚 certain is that beyond management, employees鈥攁nd even consumers鈥攚ill play an active role in deciding the future of this powerful technology鈥攁 testament, again, to the need for a diverse set of views, perspectives, and backgrounds to solve these challenging problems.