Data Bias Quotes

There is a rampant tendency in any industry where someone is trying to sell something with a bunch of data, where they cherry pick a little bit... bias a little bit. This becomes quite easy when there is an enormous amount of data to cherry pick from.

If you're not thinking about the way systemic bias can be propagated through the criminal justice system or predictive policing, then it's very likely that, if you're designing a system based on historical data, you're going to be perpetuating those biases.

If the data do not prove that indexing wins, well, the data are wrong.

I think big data companies only like good news. So I think they're just hoping that they don't get sued, essentially.

Would people cheat on climate science? Sure. Because all it is a model into which there are 2,000 variables, and if I want this outcome, I nudge that one up a little and down a little bit, and there you go.

It amazes me how people are often more willing to act based on little or no data than to use data that is a challenge to assemble.

Algorithms learn by being fed certain images, often chosen by engineers, and the system builds a model of the world based on those images. If a system is trained on photos of people who are overwhelmingly white, it will have a harder time recognizing nonwhite faces.

Biases and blind spots exist in big data as much as they do in individual perceptions and experiences. Yet there is a problematic belief that bigger data is always better data and that correlation is as good as causation.

Numbers can't speak for themselves, and data sets - no matter their scale - are still objects of human design.

I want to talk about privacy, the quality of the information you receive, whether it's neutral or commercial or pointed, bringing consciousness to the lack of neutrality in the algorithms.

People think 'big data' avoids the problem of discrimination because you are dealing with big data sets, but, in fact, big data is being used for more and more precise forms of discrimination - a form of data redlining.

Data will always bear the marks of its history. That is human history held in those data sets.

If we start to use social media data sets to take the pulse of a nation or understand a crisis - or actually use it to deploy resources - we are getting a skewed picture of what is happening.

Machine learning is looking for patterns in data. If you start with racist data, you will end up with even more racist models. This is a real problem.

Big Data is neither color-blind nor gender-blind. We can see how it is used in marketing to segment people.

The promoters of big data would like us to believe that behind the lines of code and vast databases lie objective and universal insights into patterns of human behavior, be it consumer spending, criminal or terrorist acts, healthy habits, or employee productivity. But many big-data evangelists avoid taking a hard look at the weaknesses.

The World Bank is the monopoly provider of poverty data and, partly due to a leadership change there, the World Bank's reporting has been heavily on the rosy side since about 2000. The Bank's cultivation of an upbeat picture affords a very interesting lesson in statistics and how you can, depending on which numbers you present and how you present them, create a more positive or more negative impression of the evolution of poverty.

Artificial intelligence is just a new tool, one that can be used for good and for bad purposes and one that comes with new dangers and downsides as well. We know already that although machine learning has huge potential, data sets with ingrained biases will produce biased results - garbage in, garbage out.

The fear isn't that big data discriminates. We already know that it does. It's that you don't know if you've been discriminated against.

Hidden biases in both the collection and analysis stages present considerable risks and are as important to the big-data equation as the numbers themselves.

We should always be suspicious when machine-learning systems are described as free from bias if it's been trained on human-generated data. Our biases are built into that training data.

Sexism, racism, and other forms of discrimination are being built into the machine-learning algorithms that underlie the technology behind many 'intelligent' systems that shape how we are categorized and advertised to.

AIs trained on open data are more likely to be neutral and trustworthy instead of biased by the interests of the corporation who created and trained them.

By construction, the world of big data is siloed and segmented and segregated so that successful people, like myself - technologists, well-educated white people, for the most part - benefit from big data, and it's the people on the other side of the economic spectrum, especially people of color, who suffer from it. They suffer from it individually, at different times, at different moments. They never get a clear explanation of what actually happened to them because all these scores are secret and sometimes they don't even know they're being scored.

I would argue that racism, for example, is a feature of machine learning - it's not a bug.