Algorithmic Bias
By: Soroush Hashemi Far
Tags: Deep Learning, Machine Learning, Algorithmic Bias, Computer Science, Algorithms

Algorithms have become the most effective elements in today’s world and they do the computations and decision makings more reliable and faster than human. They make decisions about the news we read, the jobs we get, the people we meet and the ads we see. According to the stories we read on Facebook social net, texts we send on Telegram messenger, photos we like on Instagram application and the search results we see on Google and etc, we generate Big data. Big data is used to make decisions about health care, employment, housing, education and policing.

Contrary to what many of us might think, technology is not objective. Algorithm’s behavior depends on people who invented them: what code they write, what data they use to train the machine learning models, and how they test the models after they’re finished. For example, Give an image classification algorithm millions of labeled cat pictures and it will be able to tell you whether a photo it hasn’t seen before contains a cat. Give a speech recognition algorithm millions of voice samples along with their corresponding written words, and it will be able to transcribe spoken language faster than most humans.

The more data you feed to your learning algorithm, The better it becomes at the task it’s designed for.

According to above sentence, any amount of data you feed your algorithm of white people, you should do the same for dark-skinned people or any amount of data you feed the algorithm including men, do the same for women and transgenders, etc.

Software programs are not free of human influence, because generally we are creating them and they behave on data that may be provided by humans, machines or a combination of both and according to them algorithms may inherit bias.

Machine learning algorithms learn and evolve based on what people do online.

For instance, a new study by Carnegie Mellon University researchers found that Google’s online advertising system showed an ad for high-income jobs to men much more often than it showed the ad to women. Algorithmic bias is getting a more serious problem than the past and governments and algorithm developers don’t care about it, or if they do, that’s not enough :)

If the bias exists inside the algorithms that make important decisions, goes unrecognized and unchecked, it could have dangerous consequences, especially for poor communities and minorities.

TED talk provided by Joy Buolamwini

Check this TED talk, provided by Joy Buolamwini, to see a real problem, where a machine doesn’t recognize black people’s face well, but it works great for white people.

Another example is a joint project by researchers at Microsoft and Boston University, in which they found sexist biases in word embedding algorithms, which are used in search engines, translation and other software that depend on natural language processing. Among their findings of the behavior of word embedding algorithms was a tendency to associate words such as “programming” and “engineering” to men and “homemaker” to women.

Algorithmic bias is not a new topic. For decades, academics and experts warned companies and algorithm developers. But what makes it critical at this time, is that these algorithms are getting bold in the everyday decisions we make.

Open source VS. algorithmic bias

One of the best ways to fight algorithmic bias is by evaluating the training data fed into machine learning models. The data itself might have a skewed distribution; for instance, programmers may have more data about United States citizens than immigrants, and about rich men than poor women. Such imbalances will cause an AI to make improper conclusions about how our society is in fact represented.

open source technology has extreme potential to help in this fight against algorithmic bias. Modern artificial intelligence is dominated by open source software, from TensorFlow to packages like scikit-learn. The open source community has already proven extremely effective in developing robust tested machine learning tools, so it follows that the same community could effectively build anti-bias tests into that same software.

Open source technology has also proven to be extremely effective for vetting and sorting large sets of data. Nothing should make this more obvious than the domination of open source tools in the data analysis market (Weka, Rapid Miner, etc.). Tools for identifying data bias should be designed by the open source community, and those techniques should also be applied to the plethora of open training data sets already published on sites like Kaggle.

People VS. algorithmic bias

Companies that develop and apply machine learning systems and government regulators, show little interest in monitoring and limiting algorithmic bias. They should pay more attention to this topic before it becomes out of our hands. Plus, Technology companies use all sorts of mathematical models and aren’t transparent about how they operate!

It would be impossible for humans to oversee every decision an algorithm makes. But companies can regularly run simulations to test the results of their algorithms.

Algorithmic bias is a human problem, not a technical one. Our thought, biases, interests, hates, etc, reflects in AI programs. The real solution is to start removing bias in every aspect of our personal and social lives. This means endorsing diversity in employment, education, politics and more. If we want to fix our algorithms, we should first fix ourselves.

Education is extremely important. We all know people who may be unaware of algorithmic bias but who care about its implications should talk to those people and explain both “how the bias is formed” and “why it matters”.