![similarity bias similarity bias](https://images.squarespace-cdn.com/content/v1/583ed05c59cc68a8c3e45c0f/1520597052092-43T7AT2H7A8WRB0QIB6U/ke17ZwdGBToddI8pDm48kMTx0rSrWh39clul-D2E9EoUqsxRUqqbr1mOJYKfIPR7LoDQ9mXPOjoJoqy81S2I8N_N4V1vUb5AoIIIbLZhVYxCRW4BPu10St3TBAUQYVKcZlHYTfp6ylcx19VHZqkSH6pOeyrZPN-UNXwRkqeBa-Hm7lbaSCbTgwVKsz4reHBH/Things-that-could-kill-you-.jpg)
While the racist rants of Tay were limited to the Twitter-sphere, it’s indicative of potential real-world implications. Tay lived a mere 24 hours, shut down by Microsoft after it had become a fairly aggressive racist. In essence, the community repeatedly tweeted offensive statements at Tay and the system used those statements as grist for later responses. Unfortunately, Tay was influenced by a user community that taught Tay to be racist and misogynistic. A clear example of this bias is Microsoft’s Tay, a Twitter-based chatbot designed to learn from its interactions with users. Bias arises based on the biases of the users driving the interaction. While some systems learn by looking at a set of examples in bulk, other sorts of systems learn through interaction. When they are trained using skewed data, or even data that is balanced but the systems are biased in decision-making, they will perpetuate the bias, as well. Learning systems used to build the rules sets applied to predict recidivism rates for parolees, crime patterns or potential employees are areas with potentially negative repercussions. While both are fixable and absolutely unintentional, they demonstrate the problems that can arise when we do not attend to the bias in our data.īeyond facial recognition, there are other troubling instances with real-world implications.
#SIMILARITY BIAS SKIN#
Nikon’s confusion about Asian faces and HP’s skin tone issues in their face recognition software both seem to be the product of learning from skewed example sets. Most recently, this kind of bias has shown up in systems for image recognition through deep learning. But if the training set itself is skewed, the result will be equally so. The thinking has been that the sheer volume of examples will overwhelm any human bias.
![similarity bias similarity bias](https://i1.rgstatic.net/publication/262562715_The_Decomposition_of_Similarity-Based_Diversity_and_its_Bias_Correction/links/0f317538055a83c1ad000000/largepreview.png)
This is not a new insight, it just tends to be forgotten when we look at systems driven by literally millions of examples. Data-driven biasįor any system that learns, the output is determined by the data it receives. But as we build and deploy intelligent systems, it is vital to understand them so we can design with awareness and hopefully avoid potential problems. These sources include the data we use to train systems, our interactions with them in the “wild,” emergent bias, similarity bias and the bias of conflicting goals. The reality is that not only are very few intelligent systems genuinely unbiased, but there are multiple sources for bias. Of course, nothing could be further from the truth. But in the middle, there is the view they will be objective. For others, it is a feature: They should be freed of human bias. And we understand that learning systems will always converge on ground truth because unbiased algorithms drive them.įor some of us, this is a bug: Machines should not be empathetic outside of their rigid point of view. We trust that smart systems performing credit assessments will ignore everything except the genuinely impactful metrics, such as income and FICO scores.
![similarity bias similarity bias](https://image.slidesharecdn.com/exploring-media-bias-with-contrast-analysis-of-semantic-similarity-cass-151219000550/95/exploring-media-bias-with-contrast-analysis-of-semantic-similarity-cass-4-638.jpg)
#SIMILARITY BIAS DRIVER#
We believe that self-driving cars will have no preference during life or death decisions between the driver and a random pedestrian. We tend to think of machines, in particular smart machines, as somehow cold, calculating and unbiased. Kris is also a professor of computer science at Northwestern University. As chief scientist and co-founder, Kris focuses on R&D at Narrative Science.