Rachel Thomas in Fast.AI on “What is the ethical responsibility of data scientists?”…What we’re talking about is a cataclysmic change… What we’re talking about is a major foreign power with sophistication and ability to involve themselves in a presidential election and sow conflict and discontent all over this country… You bear this responsibility. You’ve created these platforms. And now they are being misused, Senator Feinstein said this week in a senate hearing. Who has created a cataclysmic change? Who bears this large responsibility? She was talking to executives at tech companies and referring to the work of data scientists.
Data science can have a devastating impact on our world, as illustrated by inflammatory Russian propaganda being shown on Facebook to 126 million Americans leading up to the 2016 election (and the subject of the senate hearing described above) or by lies spread via Facebook that are fueling ethnic cleansing in Myanmar. Over half a million Rohinyga have been driven from their homes due to systematic murder, rape, and burning. Data science is foundational to Facebook’s newsfeed, in determining what content is prioritized and who sees what….
The examples of bias in data science are myriad and include:
- Google Photos automatically labeling Black people as “gorillas”
- Software to assesses criminal recidivism risk that is twice as likely to mistakenly predict that Black defendants are high risk
- Google’s popular Word2Vec language library creating sexist analogies such as man→computer programmer :: woman→homemaker.
- Neural networks learning that “hotness” is having light skin
- An app to compare job candidates’ word choice, tone, and facial movements with current employees, which Princeton Professor Arvind Narayanan described as AI whose only conceivable purpose is to perpetuate societal biases
- Google Translate converting gender neutral sentences to “He is a doctor. She is a nurse”…
You can do awesome and meaningful things with data science (such as diagnosing cancer, stopping deforestation, increasing farm yields, and helping patients with Parkinson’s disease), and you can (often unintentionally) enable terrible things with data science, as the examples in this post illustrate. Being a data scientist entails both great opportunity, as well as great responsibility, to use our skills to not make the world a worse place. Ultimately, doing data science is about humans, not just the users of our products, but everyone who will be impacted by our work. (More)”.