Monetized XAI could manipulate users and undermine algorithmic trust

AI-powered decision-making systems are increasingly embedded in hiring, lending, education, and healthcare, prompting a parallel debate over the legitimacy of the decisions they produce. Now, a groundbreaking ethical foresight study warns of a new and potentially corrupting frontier in explainable artificial intelligence (XAI). The paper, titled “Monetization Could Corrupt Algorithmic Explanations”, was published in AI & Society in 2025. It explores a speculative but technically feasible business model: monetizing AI-generated explanations by integrating them with targeted advertising.

Through a conceptual artifact called the “explanation platform,” the study presents both the economic logic and societal risks of turning algorithmic transparency into a revenue-generating tool. It offers a sharp critique of what may unfold when XAI is no longer motivated by ethics or regulation, but by market competition and advertising revenue.

What is the explanation platform and why does it matter?

The study proposes an “explanation platform” - a digital infrastructure designed to deliver algorithmic explanations bundled with personalized ads. This platform would operate in domains where algorithmic decisions are made, such as employment rejections, loan denials, or university admissions. When a person requests an explanation for a negative decision, they would receive a counterfactual explanation accompanied by a targeted advertisement, for example, a job rejection explanation suggesting the applicant lacked Python skills, followed by an ad for a Python course.

The authors outline how the platform could economically incentivize wider deployment of XAI technologies. Organizations could receive a share of ad revenue for each explanation delivered, transforming previously opaque decisions into ad-monetizable moments. In turn, advertisers could reach highly motivated consumers at a psychologically receptive point, those actively seeking to improve their outcomes.

But with monetization comes distortion. The study warns that explanation providers, entities controlling both the algorithmic decision and its explanation, could begin optimizing explanations for advertising revenue rather than user understanding or fairness. As a result, explanations could become technically valid but strategically manipulative, emphasizing costly actions that benefit advertisers rather than the most feasible recourse for users.

How can economic pressures distort the purpose of explanations?

The foresight analysis raises multiple ethical alarms. First, it explores the “multiplicity problem”: most AI systems can generate multiple plausible explanations for the same decision. This creates room for cherry-picking the most profitable ones—those more likely to prompt a user to buy a product or service. A job rejection explanation might cite “lack of MBA” not because it's the most relevant or actionable insight, but because MBA programs pay higher ad rates.

The paper describes several additional risks:

• Spam Explanations: Explanations padded with unnecessary but monetizable suggestions, diluting their clarity and usefulness.

• Raised Decision Thresholds: Platforms might increase rejection rates just to create more opportunities for ad-linked explanations.

• Deliberately Weakened Predictive Models: If monetized explanations are more profitable than correct decisions, providers may undercut algorithmic accuracy.

• Fraud and Fake Ads: Scammers could exploit explanation platforms to serve deceptive ads under the guise of algorithmic feedback.

The study draws comparisons to known advertising abuses on platforms like Google and Facebook, warning that algorithmic explanations, once regarded as a tool for transparency, could be weaponized in similar ways.

Can monetized XAI serve the public good, or will it erode trust?

The study offers a balanced view. It acknowledges the potential upside of monetized XAI, particularly in extending access to feedback and algorithmic recourse for marginalized users. For job applicants, students, or borrowers, receiving any explanation, albeit imperfect, may be preferable to opaque rejection. Monetization, the authors argue, could incentivize companies to provide explanations where they previously avoided doing so.

However, the paper emphasizes that motivation matters. If explanations are driven by profit rather than respect for user autonomy and dignity, their societal role changes fundamentally. Drawing on Kantian ethics and democratic theory, the authors argue that explanations should serve the public interest, not commercial goals. Turning them into advertising channels could undermine their legitimacy and reproduce the black-box opacity XAI was intended to solve.

The study urges policymakers to preemptively consider safeguards, including third-party audits, explanation quality scores, and user-centered design standards. Without such measures, explanation platforms risk becoming the next generation of surveillance capitalism, appearing transparent while subtly manipulating human behavior.