0625 / about AI bias

 기술을 사용하는 사람들은 늘 기술을 사용함에 있어 책임감을 가져야 한다고 생각한다. 모든 개발 분야에 해당하는 생각이지만, 특히나 어떠한 데이터를 제공하는 서비스 개발에 있어서는 더더욱. 개발자가 제공한 데이터가 사용자의 단 하나의 데이터가 될 수도 있는 가능성-사용자가 서비스에 대한 신뢰로 단 한번만 사용하고 결론을 내려버리는. 을 생각해본다면 `좋은 데이터`에 대한 고민에 두 어깨가 무겁게 고민하는 개발자들이 이 세상에 가득 넘쳤으면 좋겠다-같이 일하고 싶어서. 멋지지 않나. 

 

좋은 데이터란?

0626의 생각/ 좋은 데이터는 `좋은 데이터셋 + 좋은 개발자 마인드` 의 콜라보라 생각한다. 사실 좋은 개발자 마인드 위에서 좋은 데이터셋들이 구축된다고 생각이 들어서, 좋은 개발자 마인드에 우선순위를 주고 싶당.

 

https://www.theverge.com/21298762/face-depixelizer-ai-machine-learning-tool-pulse-stylegan-obama-bias

What a machine learning tool that turns Obama white can (and can’t) tell us about AI bias

 

- 위의 기사에서 발췌

Input a low-resolution picture of Barack Obama, the first black president of the United States, into an algorithm designed to generate depixelated faces, and the output is a white man.

 

As one popular tweetquoting the Obama example put it: “This image speaks volumes about the dangers of bias in AI.” => `An image of @BarackObama getting upsampled into a white guy is floating around because it illustrates racial bias in #MachineLearning.`

 

we need to know a little a bit about the technology being used here. In order to turn a low-resolution image into a high-resolution one, the software has to fill in the blanks using machine learning. In the case of PULSE, the algorithm doing this work is StyleGAN, which was created by researchers from NVIDIA.

 

PULSE’s creators say the trend is clear: when using the algorithm to scale up pixelated images, the algorithm more often generates faces with Caucasian features. “This bias is likely inherited from the dataset StyleGAN was trained on [...] though there could be other factors that we are unaware of.”

 

On a technical level, some experts aren’t sure this is even an example of dataset bias. The AI artist Mario Klingemann suggests that the PULSE selection algorithm itself, rather than the data, is to blame.

 

Many commercial AI systems, though, are built directly from research data and algorithms without any adjustment for racial or gender disparities. Failing to address the problem of bias at the research stage just perpetuates existing problems.

 

In this sense, then, the value of the Obama image isn’t that it exposes a single flaw in a single algorithm; it’s that it communicates, at an intuitive level, the pervasive nature of AI bias. What ithides, however, is that the problem of bias goes far deeper than any dataset or algorithm. It’s a pervasive issue that requires much more than technical fixes.

 

As one researcher, Vidushi Marda, responded on Twitter to the white faces produced by the algorithm: “In case it needed to be said explicitly - This isn’t a call for ‘diversity’ in datasets or ‘improved accuracy’ in performance - it’s a call for a fundamental reconsideration of the institutions and individuals that design, develop, deploy this tech in the first place.”