What role does Ethics & Trust play in Artificial Intelligence ?

Today, AI-powered systems are routinely being used to support human decision-making in a multitude of applications. Yet broad adoption of AI systems will not come from the benefits alone. Many of the expanding applications of AI may be of great consequence to people, communities, or organizations, and it is crucial that we be able to trust their output. Trusting

a decision of an AI system requires more than knowing that it can accomplish a task with high accuracy; the users will want to know that a decision is reliable and fair, that it can be accounted for, and that it will cause no harm. They will need assurance that the decision cannot be tampered with and that the system itself is secure. As we advance AI capabilities, issues of reliability, fairness, explainability, and safety will be of paramount importance.

Bias: In order to responsibly scale the benefits of AI, we must ensure that the models we create do not blindly take on our biases and inconsistencies, and then scale them more broadly through automation. The research community has made progress in understanding how bias affects AI decision-making and is creating methodologies to detect and mitigate bias across the lifecycle of an AI application: training models; checking data, algorithms, and service for bias; and handling bias if it is detected. While there is much more to be done, we can begin to incorporate bias checking and mitigation principles when we design, test, evaluate, and deploy AI solutions. Here are some examples -

·     If a machine is trained to identify the best college recruits, based on the backgrounds of its top students, good candidates could be excluded because they were home schooled, or didn’t go to a private high school.

·     Algorithms using hiring data to vet candidates for a company with a median age of 35 could eliminate qualified candidates in their late forties.

·     A particular ethnicity may statistically have a lower population of college graduates than another. If “education level” carries more weight in loan determination than other factors, it could impact that ethnicity’s ability to get a loan.

Explainability: Another issue that has been at the forefront of the discussion recently is the fear that machine learning systems are “black boxes,” and that many state-of-the-art algorithms produce decisions that are difficult to explain. A significant body of new research work has proposed techniques to provide interpretable explanations of “black-box” models without compromising their accuracy. These include local and global interpretability techniques of models and their predictions, visualizing information flow in neural nets, and even teaching explanations. We must incorporate these techniques into AI model development workflows to provide diverse explanations to developers, enterprise engineers, users, and domain experts.

Security: It has also been shown that deep learning models can be easily fooled into making embarrassing and incorrect decisions by adding a small amount of noise, often imperceptible to a human. Exposing and fixing vulnerabilities in software systems is a major undertaking of the technical community, and the effort carries over into the AI space. Recently, there has been an explosion of research in this area: new attacks and defenses are continually identified; new adversarial training methods to strengthen against attack and new metrics to evaluate robustness are being developed. We are approaching a point where we can start integrating them into generic AI DevOps processes to protect and secure production-grade applications that rely on neural networks.

Reliability: Human trust in technology is based on our understanding of how it works and our assessment of its safety and reliability. We drive cars trusting that the brakes will work when the pedal is pressed. We undergo laser eye surgery trusting the system to make the right decisions. In both cases, trust comes from confidence that the system will not make a mistake thanks to extensive training, exhaustive testing, experience, safety measures, standards, best practices and consumer education. Many of these principles of safety design apply to the design of AI systems; some will have to be adapted, and new ones will have to be defined. For example, we could design AI to require human intervention if it encounters completely new situations in complex environments. And, just as we use safety labels for pharmaceuticals and foods, or safety datasheets in computer hardware, we may begin to see similar approaches for communicating the capabilities and limitations of AI services or solutions. Finally, it is worth emphasizing that deciding on whom to trust to train our AI systems will be the most consequential decision we make in any AI project.

For better or worse, artificial intelligence is not simply becoming a general purpose technology (like steam power or electricity). It is, more essentially, becoming a gatekeeper technology that uniquely holds the key both to the potential for the exponential advancement of human well-being and to possibilities for the emergence of significant risks for society’s future. It is incumbent on us to prioritize considerations of the ethical purposes and values behind the trajectories of our technological advancement, that we, as vested societal stakeholders, will be able to take the reins of innovation and to steer the course of our algorithmic creations in accordance with a shared vision of what a better human future should look like.