"When you browse online for a new pair of shoes, pick a movie to stream on Netflix or apply for a car loan, an algorithm likely has its word to say on the outcome.
The complex mathematical formulas are playing a growing role in all walks of life: from detecting skin cancers to suggesting new Facebook friends, deciding who gets a job, how police resources are deployed, who gets insurance at what cost, or who is on a "no fly" list.
Algorithms are being used—experimentally—to write news articles from raw data, while Donald Trump's presidential campaign was helped by behavioral marketers who used an algorithm to locate the highest concentrations of "persuadable voters."
But while such automated tools can inject a measure of objectivity into erstwhile subjective decisions, fears are rising over the lack of transparency algorithms can entail, with pressure growing to apply standards of ethics or "accountability."
Data scientist Cathy O'Neil cautions about "blindly trusting" formulas to determine a fair outcome.
"Algorithms are not inherently fair, because the person who builds the model defines success," she said.
O'Neil argues that while some algorithms may be helpful, others can be nefarious. In her 2016 book, "Weapons of Math Destruction," she cites some troubling examples in the United States:
- Public schools in Washington DC in 2010 fired more than 200 teachers—including several well-respected instructors—based on scores in an algorithmic formula which evaluated performance.
- A man diagnosed with bipolar disorder was rejected for employment at seven major retailers after a third-party "personality" test deemed him a high risk based on its algorithmic classification.
- Many jurisdictions are using "predictive policing" to shift resources to likely "hot spots." O'Neill says that depending on how data is fed into the system, this could lead to discovery of more minor crimes and a "feedback loop" which stigmatizes poor communities.
- Some courts rely on computer-ranked formulas to determine jail sentences and parole, which may discriminate against minorities by taking into account "risk" factors such as their neighborhoods and friend or family links to crime.
- In the world of finance, brokers "scrape" data from online and other sources in new ways to make decisions on credit or insurance. This too often amplifies prejudice against the disadvantaged, O'Neil argues.
Her findings were echoed in a White House report last year warning that algorithmic systems "are not infallible—they rely on the imperfect inputs, logic, probability, and people who design them."