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What’s Trending in Explainable AI

  • January 6, 2022

Artificial Intelligence (AI) continues to fascinate and rule the world of data science. It has been the one technology that had impacted the maximum number of domains in the last 10 years – and apparently, will continue doing so throughout the current decade. Coupled with Machine Learning (ML), AI-based algorithms have proved to be a game changer in data-based decision-making and predictive models. However, for business value and critical operations, AI algorithms cannot function as an inscrutable black box or operate entirely autonomously without any kind of check point. It is now expected that organizations developing and/or implementing complex AI systems need to factor in explainability into their models to eliminate future complications.

Understanding Explainable AI

Explainability is one of the key ethical issues for AI. Any machine that goes wrong should be corrected. But for that, you need to find out why it is malfunctioning in the first place. Since AI systems work through a complex chain of algorithmic systems, traceability must be ensured to identify the root cause of any harm. This is precisely the reason why the US Department of Defense has recommended using only such AI where a human operator would always be able to follow the reasoning and understand the kill-chain process.

An ethical AI system, should always be transparent about its source data, resulting data, how the algorithms work, and why.Al is capable of accessing gigantic volumes of seemingly disconnected data from widely varied sources, and identify patterns in seconds –making them immensely faster and powerful than humans in data-based reasoning. Explainability provides a level of transparency that people can trust. It assures them that the organization is serious about data protection and still deliver value.

It looks like Explainable AI – now called “XAI” in its shorter form – is going to be in focus in the new year.

Upcoming XAI Trends

  • The biggest focus for XAI researchers will be on eliminating Unconscious Biases from machine systems. Unconscious or algorithmic biases are built into AI applications no matter how sophisticated the system is. ML or deep learning algorithms are bound to inherit unconscious biases from data sets used to train them. And when complete black box solutions introduce biases and make errors, the results can be serious. Explainable AI systems can be architected in a way to minimize bias dependencies.
  • The market for explainable AI solutions is set to double as industries adopt responsible AI solutions that relies on fairness and transparency as consistent practices. Organizations that drag their feet will face increasing scrutiny as AI continues to permeate our society, and people demand greater transparency.
  • With companies desperate to improve their XAI capabilities it is expected that there will be a lot of XAI start-up acquisitions in an attempt to poach talent – justlike Meta’s acquisition of AI.Reverie in October 2021.
  • Global market research companyForresterpredicts creative AI systems that pivot on XAI will win dozens of patents in 2022.
  • Analysts at International Data Corporation (IDC) predicts that by 2025, 40% of G2000 companies will be forced to redesign their approaches to algorithmic decision-making, providing better human oversight and explainability.
  • To maintain a maximum explainable status, we will be witnessing a conscious effort to train the AI model continuously. The intention will be to keeping the system updated so that it can support workers as situations evolve, engineering out any bias while preserving data protection.
  • One key challenge in XAI will be to identify the risk factors that will help AI systems avoid bias. Essentially, explainability should go beyond just revealing what is happening, rather it must provide actionable insights that developers or owners of models can act on. This is especially important for addressing regulatory challenges, such as non-discrimination requirements.
  • Researchers will focus on developing constraints related to what humans are capable of interpreting, and incorporating these constraints into models, so that the results are more meaningful.
  • A long-term goal for developers working with Explainable AI will be to help eradicate financial crime, especially because in the financial industry success or failure hinges on predicting market shifts caused by external events. 
  • While developing explainable solutions is one side of the coin, another aspect of the problem is to understand why machine systems behave the way they do. This is, in a way, reverse engineering of sorts where humans try to learn from ML models. As some experts have commented, this might be considered the “final test of interpretability”.
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