Foley

One reason:  A natural human fear of trusting AI’s vaunted omniscience, whether for individuals or groups.  AI is a blessing for business and big data analysis of every kind, the support of transportation, medical, and industrial applications, and making endless myriad personal and professional tasks and desires easier to achieve, more accessible, or in some cases obsolete for those of us who are mere carbon units.  Its information collation, analysis, and delivery abilities alone are without precedent.  But once AI is offered as a tool, not to inform our larger decisions but to make them, we start asking more questions. OpenAI’s ChatGPT chatbot —capable of providing and instant draft essay of legal analysis, is just the latest headline to illustrate what is coming.  As their website explains:

The development of AI as a commercialized tool is just getting going.  Its current market size in the U.S. is just over $59 billion and is expected to experience compound annual growth of over 40 percent, hitting a market size of over $400 billion in the next six years . I t would appear we are on the cusp of the genuine rise of AI in every aspect of our lives.  The more it seeps into our culture, the more that people (living in still-functioning democracies) will demand legal guarantees of protection from its endemic presence and control.

A key thing to recognize is that it is impossible to fully eliminate biases from data, but here are some ways to detect pre-existing biases in AI solutions.  First, as humans we all have preferences, likes, dislikes and differing opinions, which can impact algorithms at any stage, e.g. data collection, data processing, and/or model analysis.  Areas that companies should analyze as possible entry points for biases include selection bias, exclusion bias, reporting bias and conformation bias .  Second, establish a governance structure to establish processes and practices to mitigate bias in AI algorithms. Third, diversify your workforce.  Diverse experiences and backgrounds (including ethnic backgrounds) enable various opportunities for people to identify forms of bias.

AI builders may answer this from the inside-out:  With progressively better software ability and validation, and professional input that controls for both legal and ethical requirements.  From the outside-in, AI in its ultimate development has been characterized as the equivalent of an extraterrestrial alien space invasion :

If the data inputs for model development are not diverse, then model output will likely be biased.  Careful selection during the data collection phase can be achieved by having enough domain knowledge on the problem at hand, to appreciate if the data collected is a good sampling of the subject matter being modeled.

One challenge to codifying fairness in AI models is finding a consensus on what is fair.  ML researcher and practitioners use fairness constraints to construct optimal ML models.  These constraints can be informed by ethical, legal, social science and philosophical perspectives.  Fairlearn is an open source toolkit that enables the assessment and improvement of fairness in AI systems.  Although useful, Fairlearn cannot detect stereotyping.

It is difficult to imagine AI without bias, since decisional AI will have to make judgments about desirable outcomes—which depends on bias.  Replacing unconscious human bias, AI will have it consciously . For example, current AI employee screening tools are designed with a bias against disparate impact on protected groups.  As the EEOC puts it:

To date no federal statutes have been passed to regulate the development and use of AI, but some guidance has been put in place.