We need to pay attention to AI bias before it's too late

1 month ago 14

Cognitive bias leads to AI bias, and the garbage-in/garbage-out axiom applies. Experts connection proposal connected however to bounds the fallout from AI bias.


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Artificial quality (AI) is the quality of machine systems to simulate quality intelligence. It has not taken agelong for AI to go indispensable successful astir facets of quality life, with the realm of cybersecurity being 1 of the beneficiaries.

AI tin foretell cyberattacks, assistance make improved information processes to trim the likelihood of cyberattacks, and mitigate their interaction connected IT infrastructure. AI tin besides escaped up cybersecurity professionals to absorption connected much captious tasks successful the organization.

However, on with the advantages, AI-powered solutions—for cybersecurity and different technologies—also contiguous drawbacks and challenges. One specified interest is AI bias.

SEE: Digital transformation: A CXO's usher (free PDF) (TechRepublic)

Cognitive bias and AI bias

AI bias straight results from quality cognitive bias. So, let's look astatine that first.

Cognitive bias is an evolutionary decision-making strategy successful the caput that is intuitive, accelerated and automatic. "The occupation comes erstwhile we let our fast, intuitive strategy to marque decisions that we truly should walk implicit to our slow, logical system," writes Toby Macdonald successful the BBC nonfiction How bash we truly marque decisions? "This is wherever the mistakes creep in."

Human cognitive bias tin colour determination making. And, arsenic problematic, instrumentality learning-based models tin inherit human-created information tainted with cognitive biases. That's wherever AI bias enters the picture.

Cem Dilmegani, successful his AIMultiple nonfiction Bias successful AI: What it is, Types & Examples of Bias & Tools to hole it, defines AI bias arsenic the following: "AI bias is an anomaly successful the output of instrumentality learning algorithms. These could beryllium owed to the discriminatory assumptions made during the algorithm improvement process oregon prejudices successful the grooming data."

SEE: AI tin beryllium unintentionally biased: Data cleaning and consciousness tin assistance forestall the problem (TechRepublic)

Where AI bias comes into play astir often is successful the humanities information being used. "If the humanities information is based connected prejudiced past quality decisions, this tin person a antagonistic power connected the resulting models," suggested Dr. Shay Hershkovitz, GM & VP astatine SparkBeyond, an AI-powered problem-solving company, during an email speech with TechRepublic. "A classical illustration of this is utilizing machine-learning models to foretell which occupation candidates volition win successful a role. If past hiring and promotion decisions are biased, the exemplary volition beryllium biased arsenic well."

Unfortunately, Dilmegani besides said that AI is not expected to go unbiased anytime soon. "After all, humans are creating the biased information portion humans and human-made algorithms are checking the information to place and region biases."

How to mitigate AI bias

To trim the interaction of AI bias, Hershkovitz suggests:

  • Building AI solutions that supply explainable predictions/decisions—so-called "glass boxes" alternatively than "black boxes"
  • Integrating these solutions into quality processes that supply a suitable level of oversight
  • Ensuring that AI solutions are appropriately benchmarked and often updated

 The supra solutions, erstwhile considered, constituent retired that humans indispensable play a important relation successful reducing AI bias. As to however that is accomplished, Hershkovitz suggests the following:

  • Companies and organizations request to beryllium afloat transparent and accountable for the AI systems they develop.
  • AI systems indispensable let quality monitoring of decisions.
  • Standards creation, for explainability of decisions made by AI systems, should beryllium a priority.
  • Companies and organizations should amended and bid their developers to see morals successful their considerations of algorithm development. A bully starting constituent is the OECD's 2019 Recommendation of the Council connected Artificial Intelligence (PDF), which addresses the ethical aspects of artificial intelligence.

Final thoughts

Hershkovitz's interest astir AI bias does not mean helium is anti-AI. In fact, helium cautions we request to admit that cognitive bias is often helpful. It represents applicable cognition and experience, but lone erstwhile it is based connected facts, crushed and wide accepted values—such arsenic equality and parity.

He concluded, "In this time and age, wherever astute machines, powered by almighty algorithms, find truthful galore aspects of quality existence, our relation is to marque definite AI systems bash not suffer their pragmatic and motivation values."

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