More organizations in this digital era target AI behavior forensic experts to combat the business reputation risk by 2023, reveals Gartner.
Over time, the trust of users in advanced technology is plunging with the increasing usage this includes automation, artificial intelligence and machine learning tools. Besides, there have been increasing incidents of data misuse and irresponsible breaches amid the pandemic.
Despite the regulatory scrutiny to combat these occurrences, Gartner anticipated that nearly 75% of organizations would now focus on hiring an AI behavior forensic expert by the end of 2023. Furthermore, the business decision-makers plan for privacy and customer trust experts in their company – who could help to reduce their brand risk and reputation.
Biasness based on data structure, gender, and location has been posing threats while training the advanced AI models. Even complex algorithms like deep learning often incorporate some highly variable and implicit interactions within their estimates – this makes them trickier to interpret.
Given the current market landscape, the experts believe that innovative solutions and higher skills are essential to help companies reduce the corporate brand risk, identify the potential sources of preferences, as well as build trust by using the AI models.
As a result, many chief data officers (CDOs) and data analytics experts across organizations are focused on onboarding more ML forensic and ethics investigators. Financial and technology companies are increasingly testing and also deploying new combinations of risk management and artificial intelligence governance solutions to tackle this. For instance, prominent enterprises like Facebook, Bank of America, NASA, Google, etc., have been appointing AI behavior forensic authorities for years now. Such professionals primarily focus on uncovering the undesired bias in artificial intelligence models right before their deployment.
The number of these experts will significantly rise over the next five years, particularly with the steep escalation in digital transformation journeys across businesses. In fact, the service providers plan to introduce new facilities in order to ensure ML models are explainable, meeting definite standards and audits.
Furthermore, many organizations have also presented dedicated AI explainability tools to support their customers and clients, categorize bias and fix them in the algorithms. Open-source solutions like Local Interpretable Model-Agnostic Explanations could help in unplanned discrimination before turning to models.
These advanced solutions can help ML investigators look at the influence of data on certain sensitive variables – including age, race, and gender on other variables. In addition, they can measure the correlation amount that variables are having with each other and find if they are readily adjusting to the expectations of the advanced model.
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In this perspective, Jim Hare, Research VP at Gartner, explained – “Data and analytics leaders must also establish accountability for determining and implementing the levels of trust and transparency of data, algorithms, and output for each use case. It is necessary that they include an assessment of AI explainability features when assessing analytics, business intelligence, data science, and ML platforms.”