While the techniques may be non-biased, once we feed in the data, assumptions, etc.
, there’s a high chance of bias appearing in the model; because these are conditions set by humans, who may project their biases into the results, even if they weren’t intending to do so.
Cathy O’Neill provides some really great examples of different types of bias that impact our society today.
As stated in her book, Weapons of Math Destruction, “when we blithely train algorithms on historical data, to a large extent we are setting ourselves up to merely repeat the past.
If we want to get beyond that, beyond automating the status quo, well need to do more, which means examining the bias embedded in the data.
The data is, after all, simply a reflection of our imperfect culture.
”A key example that O’Neill writes about is how recidivism (the tendency of a convicted criminal to re-offend) risk algorithms are inherently discriminatory.
She states that when you have a dataset that has a “favored” group of people and a “discriminated” group of people, and you’re deciding on an outcome that has historically been awarded to the favored group more often – in this case, it would be a low recidivism risk rating – then you cannot expect to maximize accuracy and keep the discrimination down to zero at the same time.
This is likely not an intentional outcome but does highlight the need for more awareness around ethical implications of models/ algorithms.
Need for a Chief Ethics OfficerInappropriate use of data science methods can lead to erroneous conclusions.
Those that work with data have a lot of power; and with that power, comes great responsibility!.There is a need for a new CEO – a Chief Ethics Officer who won’t have the option to say “it’s not my job to think about ethics”.
The role might already exist in some companies in the form of a Chief Compliance Officer, Chief Privacy Officer, Chief Risk Officer, etc.
If that’s the case, then the responsibilities of these roles need to be officially defined to include the monitoring of ethical implications.
Bio: Kate Strachnyi is the author of The Disruptors: Data Science Leaders and Journey to Data Scientist and currently working on a few new books: “Mothers of Data Science”, “Data Literacy for Kids” and Vol II of The Disruptors: Data Sciencce Leaders.
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