Although AI has become ubiquitous, it’s also become incredibly complex. In fact, it’s become too complex for humans—even for data scientists and engineers—to comprehend the calculations that AI systems generate. Moreover, ethical concerns have been raised, particularly surrounding bias inherent to the learning models that underlie AI. Explainable AI (XAI) helps solve both of these problems.
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What is XAI?
Explainable AI is a set of tools and frameworks that help humans understand, interpret, and trust the results that machine learning models generate. XAI is a meta layer extension of AI that describes the AI model itself, including potential biases.
How XAI Works
XAI breaks down the various features and their attributions to show you what exactly is impacting the model’s forecasts.
Tools like IBM’s AI Explainability 360, Fiddler Explainable AI, and Google’s Vertex AI provide detailed, under-the-hood information on model evaluation metrics and feature attributions. This information gives a better understanding of how much each input feature impacts the model’s predictions.
XAI works similarly to course grades. Test scores with a 25-percent weight will more drastically impact the final grade than participation grades that carry a 5-percent weight towards the total grade.
Why XAI Matters for Your Company
When users understand how certain predictions are made, they can take corrective action as needed before the model gets deployed at scale. This is crucial when dealing with bias and the ethics of AI because companies can identify and root out potential discrimination against certain groups and demographics.
XAI positively impacts AI’s overall performance, makes decision making more transparent, and ensures regulatory compliance.
Optimal AI performance
XAI enables better understanding of a model’s behavior so that developers can be rest assured that the system is working properly. Knowledge of the logic behind AI algorithms allows developers to more easily address and expose bugs and biases to improve model performance for more accurate insights.
XAI describes the accuracy, fairness, and transparency of the model whose results, in turn, drive decision making in your company. As a result, with explainable AI, a company can trust that it’s making decisions based on solid insights.
Proper algorithm functionality might be necessary to meet regulatory standards, depending on the industry. Articles 13 and 22 of the EU’s General Data Protection Regulation (GDPR), for example, stipulate that individuals must have recourse to meaningful explanations of automated decisions that affect them. If a decision is challenged, XAI provides the background data documentation that led to the original decision.
XAI for Fairer AI in Different Sectors
XAI for HR
Applying XAI to your company’s applicant tracking system can identify potential discriminatory patterns before unfairly disqualifying a swath of qualified candidates.
XAI for banking
Banks benefit from explainable AI to discern whether discriminatory patterns are at work in AI models that approve credit card applications, credit line increases, and more. Apple learned this lesson the hard way when its machine learning model for approving or denying increased credit limits allegedly discriminated based on gender.
XAI for education
AI has become a prominent feature in test proctoring by monitoring typing patterns, eye movements, and other user behavior. In doing so, however, AI propagates what is called “digital ableism,” a dismissal of the fact that humans use and interact with technology differently, based on what their bodies can(not) do. The AI-powered proctoring system flags users who are differently abled and/or have medical conditions that affect the monitored activities.
XAI for law enforcement
Since AI-backed facial recognition technology fails to correctly identify individuals of color, this has devastating implications for use in law enforcement, as individuals are falsely suspected of committing crimes.
Examples of ableist and racial bias in AI for educational and law enforcement use cases underscore the need for diverse inputs and training data in the development of learning models and, in addition, explainable AI that renders these learning models more transparent.
XAI for the military
The US Air Force uses drones equipped with Z Advanced Computing’s XAI-powered 3D image recognition technology to maintain accountability for fatalities that these drones incur.
Challenges of XAI
Lack of common standards
In determining the explainability of AI algorithms, the developer community needs a common framework for what explainability means and an accompanying set of metrics for what qualifies as a good explanation.
Algorithmic explanations need to be accessible not only to experts, but to non-technical audiences as well.
Explainable AI serves as an antidote to AI’s complex algorithms as well as its biases by providing a meta layer of explanation, allowing for human interpretation. However, XAI’s explanation of algorithms opens the door for “adversarial attacks” or human manipulation to garner desirable results that influence decision making.
Rendering models more comprehensible may ultimately sacrifice system performance, leading to inaccurate results from which decisions are made.
Research points toward the need for further study of the relationship between data fusion (the merging of several data sources, data privacy, and XAI. As of now it’s unclear to what extent explainability compromises data privacy.
Future of XAI
Ethics will continue to figure in conversations surrounding AI use. As AI becomes too complex for the human brain, making its potential biases even more hidden, explainable AI is a step in the right direction, paving the way for more responsible use of AI.
Expect to see companies improve their models and governments introduce more regulation, especially regarding facial recognition technology, in order to catch up to rapid AI innovation and adoption. Explainable AI will become increasingly necessary for companies’ ethical development and application of AI.