Algorithmic Accountability

Relevant Questions

Asking questions is one of journalism’s core activities: By asking pertinent questions, journalists play vital roles in exposing abuses of authority and informing the public. This section provides an introduction to important questions to think about and ask when reporting on algorithms.

The Big Question:

Despite their actual and potential benefits, the algorithms that are the building blocks of artificial intelligence can amplify inequalities and mask injustices.

Reporters can help address these concerns by consistently asking how the use of algorithms shifts power.

Reporting that addresses this basic question can help members of the public distinguish between genuine benefits and promotional hype, on one hand, and actual risks versus dystopian panic, on the other hand.

More specifically, Ask how algorithms shift power:

  • In society at large?
  • Among specific categories of people?
  • Within journalism and between the profession and other key social institutions, such as Big Tech?

The questions that follow are adapted from ACLU Washington’s Algorithmic Equity Toolkit.

  • What can this algorithm do?
  • How does this algorithm work?
  • What are the challenges posed by this particular use of this algorithm?
  • What guardrails are necessary to make its use safe and equitable?
    • Is the system only being used for the specific purpose for which it was designed?
  • How do people perceive this algorithm?
    • Is its operation invisible or unclear to the people affected by it?
  •  What data does the system use for training?
    • Who gathered this data?
    • Is the data used appropriate for this purpose?
    • Have the system’s developers obtained the proper permissions or rights for this data? Especially, are any copyright laws potentially being violated?
    • Has the data been audited to assure that it does not reflect discriminatory practices such as racial profiling?
    • Is the company behind the algorithm transparent about its practices regarding development, privacy, and security?
  • What type of content does this system amplify? What type of content does this system marginalize or block?

  • Under what conditions does it make mistakes?
  • What types of mistakes does it make?
  • How often do these mistakes occur?
  • How are system users trained to recognize and resolve errors?
  • What reporting processes assure that the public is informed?
  • Is there community oversight in monitoring errors and outcomes?
  • What protections are there for whistleblowers?
Scale

Ask Yourself:

Does my reporting spread unrealistic expectations
 (whether positive or negative) about algorithms?

Beware of reporting that adapts either a utopian or dystopian understanding of what AI can do (boomers versus doomers). Focus instead on what systems actually do (or fail to do) and the “guardrails” necessary to make their use safe and equitable for everyone.

Who Benefits? At What Cost?

Beyond how the system works (or, is intended to work), it’s important to consider:

  • Who developed this system?
  • Who stands to profit from its use?
  • How might it disrupt jobs, devalue labor, or degrade working conditions?

Generative AI systems often come at a cost to the planet.

  • Does this system degrade the environment?
  • What are this system’s carbon and water footprints?

What About Legal and Copyright Implications?

As AI becomes more embedded in newsrooms, be alert to the following concerns:

  • Who owns AI-generated content? Not all jurisdictions recognize copyright for AI-generated work.
  • If AI-generated content spreads false information, who is legally responsible?
  • How will disclosure of AI-generated content affect pay structures for independent journalists?
  • Do copyright laws apply when journalists use AI to summarize report or scrape data?

These are thorny questions that resist simple summary. Here is one primer and FAQ that outlines the main concerns.