Nieman Foundation at Harvard
Algorithms shape large parts of everyday life: our interactions with other people, what products we purchase, the information we see (or don’t see), our investment decisions and our career paths. And we trust their judgment: people are more likely to follow advice when they are being told that it came from an algorithm rather than a human, according to a Harvard Business School study.
Despite our growing reliance on algorithms, the Pew Research Center found that Americans are concerned with the fairness and effectiveness of computer programs that make important decisions in their lives. 58 percent feel that algorithms are likely to reflect some level of human bias.
And they’re right. Even though algorithms can seem “objective” and can sometimes even outperform human judgment, they are still fallible. The notion that algorithms are neutral because math is involved is deeply flawed. After all, algorithms are based on data created by humans — and humans make mistakes and have biases. That’s why American mathematician Cathy O’Neil says: “Algorithms are opinions embedded in code.”
Machine bias can have grave consequences. A hiring algorithm at a large tech company might teach itself to prefer male applicants over female applicants. Policing software that conducts risk assessments might be biased against black people. And a content recommendation algorithm might amplify conspiracy theories.
In a world where key decisions are increasingly driven by algorithms, news organizations are taking a closer look at how these systems work and how they impact individuals and society.
Algorithms can be difficult to explain to readers — they can require technical domain knowledge to understand, they can change rapidly, and private companies often keep the details of their operation under wraps. In some cases, even the companies or government agencies that own the algorithms might not have full visibility into their inner workings because the systems are not developed to explain their decisions. The complexity of algorithmic calculations means it can be very challenging to ascertain exactly how a certain result was reached — meaning those using the system are at some level trusting it blindly. That’s especially worrisome when we think about algorithms being used by governments to make wide-ranging decisions like assessing the safety of airlines or bridges.
Reporters investigating how algorithms work try to look inside them. That’s what “algorithmic transparency reporting” is all about: shining a light into the opaque nature of these black boxes, trying to track the steps from input to output that cannot be seen in full. To do this, journalists are expanding their toolbox and collaborating with data scientists and technologists.
When algorithms fail, they can lead to discrimination, financial losses, privacy breaches, and more. These are all instances worth investigating for journalists. The algorithms beat is relatively young, but it’s likely to become more and more important as organizations and governments adopt algorithmic technologies more widely.
What are the sorts of algorithms that might be of journalistic interest? Think of cases where traditional fields adopt algorithms, or algorithms spur new industries; when algorithms make mistakes or demonstrate biases; when advancements in research unlock new algorithmic possibilities; or when algorithms are being regulated by governments.
Algorithms are newsworthy when they begin to disrupt existing industries or launch new ones. The autonomous car industry, for example, has been driven by advances in algorithms for navigation, object detection, and other tasks. In the future, it will be relevant for journalists to document these developments, assess the scope of their economic and social impact, and evaluate potential risks in their usage of algorithms (more on this later).
The way algorithms function may also sometimes conflict with existing social values or legal norms, as outlined by Nick Diakopoulos, professor at Northwestern University’s School of Communication in his foundational paper “The Algorithms Beat.” Privacy, for example, is a norm that algorithms can easily violate. Vox has reported on how personalization algorithms may make recipients feel that their identity or data has been compromised.
Algorithms become newsworthy most often when they make mistakes. When an algorithm deployed at scale does something that it’s not supposed to, that failure can have immense consequences — and challenge public perceptions about algorithms’ fallibility. Since algorithms by nature operate with minimal human oversight and are often perceived as objective, reporting on their failures is a necessary journalistic challenge.
Examples of algorithmic failures include when Google Translate makes incorrect translations , as reported by Mental Floss, or when Apple Maps directs drivers to the wrong location, as highlighted by Forbes. Algorithmic errors can become relevant when they systematically disadvantage certain groups, reflecting the bias of data inputs.
Algorithms can also have implicit negative consequences, even if they function as programmed. These errors may speak to limitations in how companies are evaluating the scope of their algorithmic impact or failures in government regulation.
One example is YouTube’s recommendation algorithm. Its goal is presumably to keep the user on the site and to generate as many views as possible by recommending videos of interest to the user. The Guardian reported that several researchers, among them a former software engineer of the company, have noticed that the platform tends to suggest videos that promote extremist views like conspiracy theories. While this might help YouTube achieve its goal of more clicks, it may also violate common perceptions of a healthy media diet and might even have implications for democracy as a whole. The company said in a blog post end of January that it would take a “closer look” at ways to reduce the spread of content that borders on violating its community guidelines and “content that could misinform users in harmful ways.”
Algorithms that function correctly can also be deployed incorrectly, manipulated or used in unintentional ways by users. For instance, Harvard Business Review reported on the variety of ways hackers game algorithmic security systems with fake data. When algorithms serve as gatekeepers, they can be susceptible to attacks from adversarial sources, including those who attempt ID theft by manipulating images on facial recognition systems. Another investigation by The Wall Street Journal explored Amazon’s efforts to prevent click farms and reviewers-for-hire from outsmarting its product-ranking system.
Journalists can play an important role in informing the public of advancements in algorithmic research that may yield new potential risks or offer solutions to old problems. For example, research developments in adaptive sampling have the potential to exponentially increase how quickly algorithms learn. And researchers have found a new way to use algorithms to help warn of heart attacks. This type of reporting can draw on traditional science and health reporting techniques to explain new methods and their potential implications to the layperson reader.
Political responses to algorithmic technologies are increasingly newsworthy, whether it’s GDPR requirements for algorithmic accountability or debates over whether governments should regulate algorithmic biases, as reported by Tech Republic. The role of reporters here would be to contextualize proposed policies by evaluating the efficacy of planned regulations. Algorithms are also increasingly the subject of litigation, and reporting on these stories requires explaining the new implications of algorithms for existing laws, which were most likely written before the popularization of computer programs.
There are many elements of an algorithm that help determine its quality — and its impacts. These are some of the attributes that can help journalists guide their research:
With more advanced computational journalism or investigative sourcing, it’s possible to expose the inner workings of algorithms or uncover algorithmic errors. Processes through which journalists across newsrooms have evaluated algorithms from the outside include:
When weighing whether to publish a story that reveals the inner workings of an algorithm, journalists should consider the consequences it might have for the organizations deploying the algorithm and to the people using or depending on it. Would disclosing how a specific algorithm works allow readers to manipulate or circumvent it in the future? Once people know which inputs and criteria a computer program takes into account, will they might try to game the system for their benefit? It’s good practice to ask questions like: How might a story on a specific algorithm expose it to manipulation? And who would benefit from that manipulation?
As algorithms are used in more areas of society, the need for newsrooms keeping those systems in check will continue to grow. Given the complexity of auditing algorithms, it’s important to consider how promoting media literacy and developing insightful journalism can be leveraged to hold AI systems accountable and citizens aware of its influences.
Francesco Marconi, Till Daldrup, and Rajiv Pant all work at The Wall Street Journal — as R&D chief, research fellow, and chief product and technology officer, respectively.
