Key Challenges in XAI: Limitations and Opportunities
XAI has been a boon to the AI industry to get an approval on the models that are developed, but still there’s a ton of challenges that can still be improved and worked on.
In a world where our reliance on artificial intelligence is growing, there is an increased need for transparency, which is where XAI comes in. To understand XAI or explainable AI, it’s important to understand what Artificial Intelligence (AI) is. In the simplest of terms, it can be described as the systems or machines that replicate or mimic human intelligence to perform tasks.
AI systems function by utilizing large sets of data as their input to produce an output, after which it analyzes its own performance and makes profitable adjustments. Inputs are transformed into outputs after the algorithm develops an understanding of the relation between various inputs’ impact on outputs, and that forms the core concept of Explainable AI.
Explainability in AI is meant to help provide insight into how models make decisions, and to enable humans to understand, manage, and trust the use and results of AI. In simple terms, it allows humans to retrace the algorithm’s thought process and understand the logic applied to their decisions, and consequently, utilize those findings to develop superior machines.
In this blog, we will try to look at some of the limitations and key challenges that XAI still has. To know more about XAI, its relevance, and its applications you can go through some of the other blogs on XAI that are available here on our site.
Key Challenges in XAI
Despite the theoretical benefits of XAI, it has certain shortcomings that are crucial to consider. For one, there is no established standard for the expectations of what explainability should achieve. Developers and users have different needs from the explanations of AI models, in terms of both technical understanding and norms and decision-making processes, based on the specific domain.
Examples of a domain could include governance, engineering, deploying, and in general society, where common AI is used. If explainability goals are reached in one domain, it is unlikely that those results will fulfill another’s goals.
In a similar broad context, when XAI is applied in real society, society’s views, values, and norms can extensively differ and be interpreted differently from other regions’ societal acceptations. For a more specific example, in a credit scoring system, a common user’s main interests from XAI could involve simply knowing which various factors led to a deduction of credit, while a developer would want more complex data: model’s outputs in hypothetical conditions, test the model’s conceptions of fairness, the value of various features, etc.
Depending on the user and circumstance, XAI would require different outcomes. Additionally, when XAI is put in practice, explainability fails to successfully meet the goals and requirements of users and impacted communities, with a higher priority placed on engineering needs.
Furthermore, the goal of XAI is to provide a purposeful and easily accessible explanation, but currently, these explanations are insufficient and cannot adequately aid developer’s efforts to modify the model for satisfactory results. This dilemma also raises another, disproportionate power dilemma between a developer and the users, who are impacted by the system.
With the increased incorporation of AI in society, whether done intentionally or not, the knowledge of the decisions made by artificial intelligence and the power to manipulate those decisions could create a dangerous power differential. Most individuals in general would not possess the expertise to comprehend the explanation and assess the fairness of the decision made, and if XAI is put into wide use, they may not even be given the opportunity to contest the outcomes of an AI system.
Fairness is also a challenge for XAI because the perception of fairness is subjective and is dependent on the data given to the machine learning algorithm. Furthermore, there is a high potential for bias within these systems, because it is difficult to ensure that the AI will not learn biased world views and project those in their results and decisions, as a result of inconsistencies within training data and general model function.
There is a lack of trust and transparency in how AI makes decisions and without explanations of how it reaches conclusions, it is unlikely customers will be able to gain confidence in the use and reliability of AI. In this case, confidentiality and transparency become an issue. Often, any given algorithm is a trade secret and confidential information, because it is regarded as a security risk. For example, if a company was required to explain the exact details of how a given AI system worked, then the concept of intellectual property becomes null. There is no legal establishment for the basis on which individuals can have the right to access a simple explanation of AI decisions.
Finally, as a result of trying to make AI more explainable, there could be tradeoffs in general performance and consistency. To successfully execute XAI, models would need to be made simpler, but complex models are more versatile and can be used in real-world situations. To ensure the continuation of high performability, even at a larger scale, complexity must be incorporated.
XAI has several hurdles to overcome, but to justify the fairness of decisions made by AI and its general use, it’s critical to overcome these challenges as we go into the future.
What is ANAI and how can we help?
ANAI is an end-to-end machine learning platform to build, deploy and manage AI models at a faster rate saving a ton of time and money spent on building AI-based systems. It enables entities to handle and process data, create exploratory and insightful visuals, and make the data ML ready.
ANAI’s AutoML pipeline utilizes the transformed data and extracts the correct features from it so that the model learns the most important details from the data. The data is then passed to the ML pipeline where various ML models are trained and only the best out of them are selected for deployment. ANAI’s MLOps allows users to keep a tab on their models even after deployment to check for model drifts and performance issues.
But due to all this automation of AI, there’s always a chance of untrust regarding the model results and as AI models are already termed black boxes because they provide no insights within their functioning, it again becomes more difficult. To solve this ANAI also has a model explanation pipeline called Explainable AI (XAI) that generates explanations of the model’s results allowing us to look behind the curtains, remove biases and other inconsistencies, and ultimately create a trustable, fair, and responsible AI system.
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