Training in algorithmic discrimination


5 values to keep in mind while thinking about algorithmic discrimination:

  1. Transparency: A lot of current problems come from the fact the process–dataset, models, usage, who, what, why, …–is kept private. Establishing some degree of transparency will alleviate inherent structural problems.
  2. Explainability: Many systems are treated as blackbox magic, and we are still unable to explain most of the results. Explainability would be essential in validating the usage.
  3. Questioning: Systematic errors often remain simply because many people readily believe algorithmic solutions as “correct” without questioning or investigating.
  4. Non-abusing: Ethical acquisition of data, just presentation and usage of results, all involve non-abuse/exploitation of individuals (data sources).
  5. Vulnerability: It is important to remember that any implementation of any degree of solution at this point will be far from complete.


While “a convening” is helpful in bringing like-minded people to a conversation or collaboration and is perhaps attuned to the democratic spirit of voluntary participation, it should be for specific issues and clear purposes. Some caveats if it were to be applied to topics such as algorithmic discrimination:

  1. Lack of diversity: These gatherings tend to attract people from similar academic standings. The problem, however, affects everyone.
  2. Preaching to the choir: A lot of these gatherings have a very ostensible political/economic stance, and participants, coming from similar backgrounds as previously mentioned, often have very similar sets of values. The event could easily become an echo chamber with no critical conversation that comes from conflicting values.
  3. Idealism: Undebated values could easily produce unrealistic solutions.
  4. Separation between implementers and the affected: The people most severely affected by the problem would not be the major makeup of these gatherings. Such solutions have a danger of not reflecting the needs of the people they intend to help.
  5. Short-term solutions (especially hackathons): People are induced to imagine solutions that would be implementable in the given timeframe of the gathering. Many of the problems, however, require a much more complex approach on a very long-term timescale.

It may be a completely different category of problem, but I vote to think for a more fundamental, mandatory training system instead of a voluntary convening. It would be something similar to anti-discrimination or sexual abuse trainings that are required for jobs, schools, etc.

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