Summary
Diakopoulos’s paper “Algorithmic Accountability” explores the broad question of how algorithms exert their potential power and are worthy of scrutiny by journalists and studies the role of computational approaches like reverse engineering in articulating algorithmic transparency. Nowadays, Automated decision-making algorithms are now used throughout businesses and governments. Given that such algorithmically informed decisions have the potential for significant societal impact, the goal of this paper is to address algorithmic accountability reported as a mechanism for articulating and elucidating the power structures, biases, and impacts that automated algorithms exercise in our society. Through the use of reverse engineering methods, the researcher conducted five cases of algorithmic accountability reporting, including autocompletion, autocorrection, political email targeting, price discrimination, and executive stock trading plans. Also, the applicability of transparency policies for algorithms is discussed along with the challenges of conducting algorithmic accountability as a broadly viable investigative method.
Reflection
I think this paper touches upon the important research question on the accountability of computational artifacts. Our society currently relies on automated decision-making algorithms on many different aspects, ranging from dynamic pricing to employment practices to criminal sentencing. It is important that developers, product managers, and company/government decision-makers are aware of the possible negative social impacts and necessity for public accountability when they design or implement algorithmic systems.
This research also makes me think about whether we need to be that strict with every algorithmic system. I think to answer the question we need to consider different application scenarios, which are not fully discussed in the paper. Take the object detection problem in the computer vision research area, for example, we have two application scenarios: one is to detect if there is a car in the image for automatic labeling, the other is to check if there is any tumor in the computed tomography for disease diagnosis. Apparently, the level of algorithm accountability is required to be much higher in the second scenario. Hence, in my opinion, the accountability of algorithms needs to be discussed under the application scenarios associated with the user’s expectations and potential consequences when the algorithms go wrong.
The topic of this research is algorithmic accountability. As far as I am concerned, accountability is a wide scope of concept, including but not limited to an obligation to report, explain, and justify algorithmic decision-making as well as mitigate any potential harms. However, I feel this paper mainly focuses on the accountability aspect of the problem with little discussion on other aspects. There is no denying the fact that transparency is one-way algorithms can be made accountable, but just as the paper puts it, “[t]ransparency is far from a complete solution to balancing algorithmic power.” I think other aspects such as responsibility, fairness, and accuracy are worthy of further exploration as well. Considering these aspects throughout the design, implementation, and release cycles of algorithmic system development would lead to a more socially responsible deployment of algorithms.
Discussion
I think the following questions are worthy of further discussion.
- What aspects other than transparency you think would be important in the big picture of algorithmic accountability?
- Can you think about some domain applications that would hardly let automated algorithms make decisions for humans?
- Do you think transparency potentially leaves the algorithm open to manipulation and vulnerable to adversarial attacks? Why or why not?
- Who should be responsible if algorithmic systems make mistakes or have undesired consequences?
In response to your third question, I think that transparency does leave algorithms open to manipulation. However, I think that it is still necessary. I think that the need to be able to understand the AI systems that we interact with is more important than the need for them to be kept secret to work marginally better. I think that a great analogy is to laws. If laws are spelled out explicitly listing the exact things you can and cannot do, it is easier for criminals to find loopholes and get around them, but I think that this is still preferable to a system where laws are open ended and you can be arrested for a much wider range of actions.
I agree with their premise that you highlighted that there needs to be more than just transparency in terms of these algorithms. There also needs to be some way for mass public opinion to enforce change upon bad actors – otherwise, and especially if they have an essential monopoly, the companies will not have to change their algorithms. A company like YouTube doesn’t have to change their algorithms even when they’ve been shown to suggest radical videos to people. It just doesn’t have to happen. It’s not just the algorithms that need to be accountable, it’s the companies who deploy them.