Responsible and Explainable AI

I would call this more broadly, 'responsible decision making' to embrace what is called 'AI' and analytical decision making.  Useful and considered look at what this means. 

Responsible AI: Bridging From Ethics to Practice   By Ben Shneiderman  via CACM

Communications of the ACM, August 2021, Vol. 64 No. 8, Pages 32-35  10.1145/3445973

The high expectations of AI have triggered worldwide interest and concern, generating 400+ policy documents on responsible AI. Intense discussions over the ethical issues lay a helpful foundation, preparing researchers, managers, policy makers, and educators for constructive discussions that will lead to clear recommendations for building the reliable, safe, and trustworthy systems6 that will be commercial success. This Viewpoint focuses on four themes that lead to 15 recommendations for moving forward. The four themes combine AI thinking with human-centered User Experience Design (UXD).

Ethics and Design. Ethical discussions are a vital foundation, but raising the edifice of responsible AI requires design decisions to guide software engineering teams, business managers, industry leaders, and government policymakers. Ethical concerns are catalogued in the Berkman Klein Center report3 that offers ethical principles in eight categories: privacy, accountability, safety and security, transparency and explainability, fairness and non-discrimination, human control of technology, professional responsibility, and promotion of human values. These important ethical foundations can be strengthened with actionable design guidelines.

Autonomous Algorithms and Human Control. The recent CRA report2 on "Assured Autonomy" and the IEEE's influential report4 on "Ethically Aligned Design" are strongly devoted to "Autonomous and Intelligent Systems." The reports emphasize machine autonomy, which becomes safer when human control can be exercised to prevent damage. I share the desire for autonomy by way of elegant and efficient algorithms, while adding well-designed control panels for users and supervisors to ensure safer outcomes. Autonomous aerial drones become more effective as remotely piloted aircraft and NASA's Mars Rovers can make autonomous movements, but there is a whole control room of operators managing the larger picture of what is happening.

Humans in the Group; Computers in the Loop. While people are instinctively social, they benefit from well-designed computers. Some designers favor developing computers as collaborators, teammates, and partners, when adding control panels and status displays would make them comprehensible appliances. Machine and deep learning strategies will be more widely used if they are integrated in visual user interfaces, as they are in counterterrorism centers, financial trading rooms, and transportation or utility control centers.

Explainable AI (XAI) and Comprehensible AI (CAI). Many researchers from AI and HCI have turned to the problem of providing explanations of AI decisions, as required by the European General Data Protection Regulation (GDPR) stipulating a "right to explanation."13 Explanations of why mortgage applications or parole requests are rejected can include local or global descriptions, but a useful complementary approach is to prevent confusion and surprise by making comprehensible user interfaces that enable rapid interactive exploration of decision spaces.   ... '

Note in particular a look at 'trustworthy certification' proposed outlines for a number of large industries, to show how this might be applied by the nature of their operation ... '

Responsible Innovation

Google reports on what they believe is responsible.   Considerable piece.   Lots of words.

An update on our work on AI and responsible innovation
Kent Walker  SVP, Global Affairs
Jeff Dean   Google Senior Fellow and SVP, Google Research and Health
Published Jul 9, 2020

AI is a powerful tool that will have a significant impact on society for many years to come, from improving sustainability around the globe to advancing the accuracy of disease screenings. As a leader in AI, we’ve always prioritized the importance of understanding its societal implications and developing it in a way that gets it right for everyone. 

That’s why we first published our AI Principles two years ago and why we continue to provide regular updates on our work. As our CEO Sundar Pichai said in January, developing AI responsibly and with social benefit in mind can help avoid significant challenges and increase the potential to improve billions of lives. 

The world has changed a lot since January, and in many ways our Principles have become even more important to the work of our researchers and product teams. As we develop AI we are committed to testing safety, measuring social benefits, and building strong privacy protections into products. Our Principles give us a clear framework for the kinds of AI applications we will not design or deploy, like those that violate human rights or enable surveillance that violates international norms. For example, we were the first major company to have decided, several years ago, not to make general-purpose facial recognition commercially available. ... " 

On Decision Responsibility

Yes, but also how decision links to further process and measured goals.

Articulation of Decision Responsibility    By Robin Hill in ACM

Remember the days when record-keeping trouble, such as an enormous and clearly erroneous bill for property taxes, was attributed to "computer error?" Our technological society fumbles the assignment of responsibility for program output. It can be seen easily in exaggerations like this, from a tech news digest: "Google's Artificial Intelligence (AI) has learned how to navigate like a human being." Oh, my. See the Nature article by the Google researchers [Google] for the accurate, cautious, descripton and assessment. The quote given cites an article in Fast Company, which states that "AI has spontaneously learned how to navigate to different places..." [Fast Company] Oh, dear.

But this is not the root of the problem. In the mass media, even on National Public Radio, I hear leads for stories about "machines that make biased decisions." Exaggeration has been overtaken by simple inaccuracy. We professionals in Tech often let this pass, apparently on the belief the public really understands that machines and algorithms have no such capacity as is normally connoted by the term "decision"; we think that the speakers are uttering our own trade shorthand. When we say that "the COMPAS system decides that offender B is more likely to commit another crime than is offender D" [ProPublica; paraphrase mine], it's short for "the factors selected, quantified, and prioritized in advance by the staff of the software company Northpointe assign a higher numeric risk to offender B than to offender D." When the Motley Fool website says "computers have been responsible for a handful of `flash crashes' in the stock market since 2010," it means that "reliance on programs that instantaneously implement someone's pre-determined threshholds for stock sale and purchase has been responsible... etc." [Motley Fool] .... "

Responsibility for Program Output

Not quite understanding what they mean by responsibility here, hard to know how it will produce unexpected results.  Which might also diminish use of analytics as well.    Certainly you could promote an awareness of the further implications of output.

Assessing Responsibility for Program Output  

Communications of the ACM, August 2018, Vol. 61 No. 8, Pages 12-13
Robin K. Hill, University of Wyoming

Remember the days when record-keeping trouble, such as an enormous and clearly erroneous bill for property taxes, was attributed to "computer error?" Our technological society fumbles the assignment of responsibility for program output. It can be seen easily in exaggerations like this, from a tech news digest: "Google's Artificial Intelligence (AI) has learned how to navigate like a human being." Oh, my. See the Nature article by the Google researchers2 for the accurate, cautious, description and assessment. The quote given cites an article in Fast Company, which states that "AI has spontaneously learned how to navigate to different places."4 Oh, dear.

But this is not the root of the problem. In the mass media, even on National Public Radio, I hear leads for stories about "machines that make biased decisions." Exaggeration has been overtaken by simple inaccuracy. We professionals in Tech often let this pass, apparently on the belief the public really understands machines and algorithms have no such capacity as is normally connoted by the term "decision"; we think the speakers are uttering our own trade shorthand. When we say "the COMPAS system decides that offender B is more likely to commit another crime than is offender D"1 (paraphrase mine), it is short for "the factors selected, quantified, and prioritized in advance by the staff of the software company Northpointe assign a higher numeric risk to offender B than to offender D." When the Motley Fool website6 says "computers have been responsible for a handful of 'flash crashes' in the stock market since 2010," it means that "reliance on programs that instantaneously implement someone's predetermined thresholds for stock sale and purchase has been responsible ... etc."

The trouble is that there is no handy way to say these things. The paraphrases here expose the human judgments that control the algorithms, but the paraphrases are unwieldy. For decades of software engineering, we have adopted slang that attributes volition and affect to programs. Observations can be found on Eric S. Raymond's page on anthropomorphization5. I doubt many hackers ascribe the intentional stance to programs; I suspect rather that programmers use these locutions for expedience, as the "convenient fictions that permit 'business as usual'."3 But the public misunderstanding is literal, and serious. .... "