Toward Algorithmic Disclosure

-- By BenWeissler - 09 Oct 2020

Should the government require internet companies to disclose the inner workings of their algorithms to the public? I argue it should, and I examine the financial disclosures mandated by securities law as a useful analogue.

The term "algorithm" is slippery, often shrouded in layers of unnecessary mystification. This essay adopts a broad definition of algorithm: any decision-making process embodied in code, typically one that uses input (data) to generate output (often predictions). An algorithm can be simple — e.g., an arithmetical formula — or complex — e.g., a machine learning model.

Algorithms have been implicated in illegal housing and employment discrimination, and elsewhere have spurred extremism and hatred. Whether it is accurate to pin such maladies on algorithms is a point I revisit at the end of this essay.

The Financial Disclosure Analogue

It's valuable to step back and ask in the abstract: why have financial disclosure rules at all? After all, in a world designed by Chicago-school economists (the "libertarian dream world"), you might expect investors, individually or in groups, to contract freely with companies and thereby obtain a bargained-for level of disclosure. In the libertarian dream world, a company's stock price might trade not only as a function of its earnings and growth prospects, but also in relation to the quality of disclosure it provides.

But we have rejected the libertarian dream world — and for good reason! Sunlight is the best disinfectant. Forcing companies to disclose financial information unlocks enormous benefits, with regard to efficiency and equity. Nobody wastes money or time bargaining for disclosure, and everybody gets to invest on a level playing field, starting from the same set of (true) information.

What Problems Does Algorithmic Disclosure Solve?

Congress should wake from its slumber and pass algorithmic disclosure legislation. Exactly who must disclose what, at what level of detail and subject to what verification, are solvable problems and the proper subject of technocratic expertise. Algorithmic disclosure would carry a number of benefits:

• First, direct legal benefits. In the libertarian dream world we currently inhabit, a plaintiff who wishes to bring a suit under the Fair Housing Act against a discriminatory housing algorithm faces an uphill battle. To survive a motion to dismiss, the plaintiff must plead with enough particularity to meet Twombly and Iqbal heightened “plausibility” standards. In all likelihood, however, the plaintiff lacks sufficient knowledge of how that black-box algorithm works in order to meet the Twiqbal bar and ever get access to discovery. Algorithmic disclosure would arm would-be plaintiffs with enough predicate information to get into court.
  • If financial disclosure and the SEA is any guide, the legal consequences of algorithmic disclosure will be far-reaching. One commentator has stated (somewhat tongue-in-cheek) that “everything is securities fraud” on the grounds that when a company does something bad, then (as companies are wont to do) does not disclose the bad thing to investors, it violates securities law. Contributing to global warming (and not disclosing it) is securities fraud, mistreating orcas (and not disclosing it) is securities fraud, and so on. A regime of algorithmic disclosure, like its financial disclosure counterpart, would create a legal dragnet, forcing companies ultimately to account for the bad things they do but omit from disclosure.

• Second, behavioral benefits which flow from algorithmic disclosure. An open admission by corporate executives, under penalty of law, that “our company’s algorithm is designed to maximize engagement by strategically funneling users down extremist rabbit holes” might cause companies to rethink their algorithms and to fully internalize the reputational costs of such admissions. On the other end of the market, frank disclosures might cause users to rethink their engagement with platforms that use manipulative algorithms.

Defending the Proposal

In the camp of shocked gaspers, we might find IPdroids claiming that a company’s algorithm is proprietary IP (a trade secret), and that the government cannot constitutionally take it via forced disclosure. Whatever the merits of this position, it can likely be overcome by careful design of the disclosures. More generally, disclosure-skeptics will have to answer for the broadly successful track record of the SEA and SEC over the past century.

The second camp, the yawners, have a deeper and more persuasive critique of algorithmic disclosure. To ventriloquize these yawners: “We should avoid ascribing to algorithms magical powers of destruction and divisiveness that they simply do not have. Especially when the source of our maladies lies elsewhere — in unchecked data collection, the centralized structure of internet services, and deeper socioeconomic malaise.” Algorithmic disclosure, however, involves no misdirection nor denial of these realities. It seeks only to narrowly improve our situation and unlock incremental benefits. Moreover, because of the ‘legal dragnet’ effect discussed above, there is reason to believe that algorithmic disclosure will cast a somewhat wider legal shadow, helping us to indirectly attack the issues yawners raise — even before our law and politics address those deeper issues directly.