Principles to limit data misuse & automation from causing (unintentional) harm or infringing on civil rights.

  1. Safe & Effective Systems
    • consult diverse stakeholders & domain experts
    • conduct pre-deployment tests, risk identification & mitigation, monitoring
    • have independent evaluation/reporting
    • halting deployment should always be an option
  2. Algorithmic Discrimination Protections
    • equitable design: should not contribute to existing societal prejudice/discrimination
    • proactive testing & independent monitoring as per (1) above
  3. Data Privacy
    • data collection should meet “reasonable expectations”
    • data collected should be minimal (“strictly necessary”)
    • users should have control (“agency”) over the use of their data
      • ask for, respect, and allow user changes to consent
      • provide user with monitoring & privacy impact reports
    • UX user experience
      /design should not obfuscate user choice
    • explanations should be brief, in plain language, and give context/reason
    • default settings shouldn’t be privacy-invasive
    • no unchecked (without diverse consultation/oversight) surveillance
    • no continuous surveillance in areas likely to reduce rights/opportunities/access (e.g. education, work, housing)
  4. Notice & Explanation
    • make all users aware (in plain language) that a partially/fully automated system is being used; how & why it impacts them
    • notify about key changes to functionality
    • meta-reporting: clarity & quality assessments (of notices/explanations) should be public
  5. Human Alternatives, Consideration, & Fallback
    • should have opt-out option
    • should have timely (accessible, maintained) human referral option
    • extra oversight for sensitive domains (e.g. criminal justice, employment, education, health)
    • public reports of above (including e.g. response times, outcomes, effectiveness)

personal opinions

  • Like all good laws, the above is lightweight and thus mostly watertight (i.e. “do not contribute to prejudice” rather than “must magically reduce prejudice”).
  • There are some loopholes: one-off (non-continuous) checked surveillance data can be used to manually (non-algorithmically) discriminate without violating the above. Presumably this would be caught under pre-AI discriminatory laws.
  • The weakest link is likely the “diversity” of oversight.
  • The title is exceedingly misleading: the focus is more on vendor responsibilities rather than (customer) rights, and on data handling & human-in-the-loop monitoring of automated systems rather than AI artificial intelligence
    per se.