High Quality Data Key to Eliminating Bias in AI

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Biases are an incurable symptom of the human decision-making process. We make assumptions, judgements and decisions on imperfect information as our brains are wired to take the path of least resistance and draw quick conclusions which affect us socially as well as financially.

The inherent human “negative bias” is a byproduct of our evolution. For our survival it was of primal importance to be able to quickly assess the danger posed by a situation, an animal or another human. However our discerning inclinations have evolved into more pernicious biases over the years as cultures become enmeshed and our discrimination is exacerbated by religion, caste, social status and skin color.

Human bias and machine learning

In traditional computer programming people hand code a solution to a problem. With machine learning (a subset of AI) computers learn to find the solution by finding patterns in the data they are fed, ultimately, by humans. As it is impossible to separate ourselves from our own human biases and that naturally feeds into the technology we create.

Examples of AI gone awry proliferate technology products. In an unfortunate example, Google had to apologise for tagging a photo of black people as gorillas in its Photos app, which is supposed to auto-categorise photos by image recognition of its subjects (cars, planes, etc). This was caused by the heuristic know as “selection bias”. Nikon had a similar incident with its cameras when pointed at Asian subjects, when focused on their face it prompted the question “is someone blinking?”

Potential biases in machine learning:
  • Interaction bias: If we are teaching a computer to learn to recognize what an object looks like, say a shoe, what we teach it to recognize is skewed by our interpretation of a shoe (mans/womans or sports/casual) and the algorithm will only learn and build upon that basis.

  • Latent bias: If you’re training your programme to recognize a doctor and your data sample is of previous famous physicists, the programme will be highly skewed towards males.

  • Similarity bias: Just what it sounds like. When choosing a team, for example, we would favor those most similar to us than as opposed to those we view as “different”.

  • Selection bias: The data used to train the algorithm over represents one population, making it operate better for them at the expense of others.

Algorithms and artificial intelligence (AI) are intended to minimize human emotion and involvement in data processing that can be skewed by human error and many would think this sanitizes the data completely. However, any human bias or error collecting the data going into the algorithm will actually be exaggerated in the AI output.

Gender bias in Fintech

Every industry has its own gender and race skews and the technology industry, like the financial industry, is dominated by white males. Silicon Valley has earned the reputation as a Brotopia due to its boy club culture.