Accountable Algorithms

Abstract

Important decisions about people are increasingly made by algorithms:

Votes are counted; voter rolls are purged; financial aid decisions are

made; taxpayers are chosen for audits; air travelers are selected for

search; credit eligibility decisions are made. Citizens, and society

as a whole, have an interest in making these processes more

transparent. Yet the full basis for these decisions is rarely

available to affected people: the algorithm or some inputs may be

secret; or the implementation may be secret; or the process may not be

precisely described. A person who suspects the process went wrong has

little recourse.

Traditionally, Computer Science addresses these problems by demanding

a specification of the desired behavior which can be enforced or

verified. But this model is poorly suited to real-world oversight

tasks: real specifications are complicated or might not be known in

advance; laws are often ambiguous precisely because it would be

politically infeasible to give a precise description of their

meaning. People do their best to approximate what they believe the law

will allow. Disputes about what is acceptable happen after-the-fact

via expensive adjudication. Actual oversight happens only rarely, if

at all.

This dissertation relates the tools of technology to the problem of

overseeing decision making processes. These methods use the tools of

computer science to ensure properties that can be proven, while

providing information necessary for a political, legal, or social

oversight process to operate effectively. First, we present an example

of the current state of the art in technical systems ensuring

accountability: a well-defined policy, specified in advance, is

operationalized with technical tools, and those same tools are used to

convince outsiders or auditors. Our system allows the accountable

execution of orders by a judge for compelled access to private records

by an investigator. Moving beyond these methods, we present a general

framework for accountable algorithms, unifying a suite of tools from

cryptography to design processes that enable after-the-fact oversight,

consistent with the norm in law and policy. Accountable algorithms can

attest to the valid operation of a decision policy even when all or

part of that policy is kept secret.