An Institutional View of Algorithmic Impact Assessments

Selbst, A. (2021). An Institutional View of Algorithmic Impact Assessments. Harvard Journal of Law and Technology, 35(10), 78. The author has indicated that paper that can be downloaded has a “draft” status.
First some general points about its relevance:
  1. Rich people get personalised one-to-one attention and services. Poor people get processed by algorithms. That may be a bit of a caricature, but there is also some truth there. Consider loan applications, bail applications, recruitment decisions, welfare payments. And perhaps medical diagnoses and treatments, depending to the source of service.  There is therefore a good reason for any evaluators concerned with equity to pay close attention to how algorithms affect the lives of the poorest sections of societies.
  2. This paper reminded me of the importance of impact assessments, as distinct from impact evaluations. The former are concerned with “effects-of-a-cause“, as distinct from the “causes-of-an-effect” , which is the focus of impact evaluations. In this paper impact assessment is specifically concerned about negative impacts, which is a narrower ambit than I  have seen previously in my sphere of work. But complementary to the expectations of positive impact associated with impact evaluations.  It may reflect the narrowness of my inhabited part of the evaluation world, but my feeling is that impact evaluations get way more attention than impact assessments. Yet once could argue that the default situation should be the reverse. Though I cant quite articulate my reasoning … I think it is something to do with the perception that most of the time the world acts on us, relative to us acting on the world.
Some selected quotes:
  1. The impact assessment approach has two principal aims. The first goal is to get the people who build systems to think methodically about the details and potential impacts of a complex project before its implementation, and therefore head off risks before they become too costly to correct. As proponents of values-in-design have argued for decades, the earlier in project development that social values are considered, the more likely that the end result will reflect those social values. The second goal is to create and provide documentation of the decisions made in development and their rationales, which in turn can lead to better accountability for those decisions and useful information for future policy  interventions (p.6)
    1. This Article will argue in part that once filtered through the institutional logics of the private sector, the first goal of improving systems through better design will only be effective in those organizations motivated by social obligation rather than mere compliance, but second goal of producing information needed for better policy and public understanding is what really can make the AIA regime worthwhile (p.8)
  2. Among all possible regulatory approaches, impact assessments are most useful where projects have unknown and hard-to-measure impacts on society, where the people creating the project and the ones with the knowledge and expertise to estimate its impacts have inadequate incentives to generate the needed information, and where the public has no other means to create that information. What is attractive about the AIA (Algorithmic Impact Assessment) is that we are now in exactly such a situation with respect to algorithmic harms. (p.7)
  3. The Article proceeds in four parts. Part I introduces the AIA, and
    explains why it is likely a useful approach….Part II briefly surveys different models of AIA that have been proposed as well as two alternatives: self-regulation and audits…Part III examines how institutional forces shape regulation and compliance, seeking to apply those lessons to the case of AIAs….Ultimately, the Part concludes that AIAs may not be
    fully successful in their primary goal of getting individual firms to consider
    social problems early, but that the second goal of policy-learning may well be
    more successful because it does not require full substantive compliance. Finally, Part IV looks at what we can learn from the technical community. This part discusses many relevant developments within technology industry and scholarship: empirical research into how firms understand AI fairness and ethics, proposals for documentation standards coming from academic and industrial labs, trade groups, standards organizations, and various self-regulatory framework proposal.(p.9)




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