How Machine Learning Trains AI to be Sexist (by Accident)


A University of Washington research team studied how computer vision algorithms handled gender predictions based on an image data set. Using a classic set of images typical in AI predictive experiments, the AI neural network predicted women to be doing traditionally "female" tasks in the images. It's not stellar that the neural network predicts that women are 33% more likely to appear in the kitchen/cooking. After all, machine bias is human bias given how machine learning works in its current iteration.

Sensitive robots can tell your gender from a handshake

Daily Mail

First results show a robot is capable of inferring someone's gender and personality in 75 per cent of cases simply by shaking hands (stock image) First results show that a robot is capable of inferring someone's gender and personality in 75 per cent of cases simply by shaking hands. The ENSTA research team have developed robots that can detect emotions and change their behaviour accordingly. The ENSTA robots detect emotions and change their behaviour accordingly. First results show a robot is capable of inferring someone's gender and personality in 75 per cent of cases simply by shaking hands.

Use Machine Learning to Recognize Images With IBM Watson


With the IBM Watson Visual Recognition service, creating mobile apps that can accurately detect and analyze objects in images is easier than ever. Lastly, add your Visual Recognition service's API key to the strings.xml The Watson Java SDK exposes all the features the Visual Recognition service offers through the VisualRecognition class. You can take a picture by simply calling the CameraHelper object's dispatchTakePictureIntent() method, so add the following code inside the event handler: The above method uses the device's default camera app to take the picture. Here's a sample face analysis result: The Watson Visual Recognition service makes it extremely easy for you to create apps that are smart and aware of their surroundings.



They're doing this with Mya, an intelligent chatbot that, much like a recruiter, interviews and evaluates job candidates. Since AI is dependent on a training set generated by a human team, it can promote bias rather than eliminating it, she adds. Grayevsky explains that Mya Systems "sets controls" over the kinds of data Mya uses to learn. This is why it's a possibility that rather than eliminating biases, AI HR tools might perpetuate them.

Guess Which Gender Trusts Artificial Intelligence More?


A majority of people are skeptical of the government adopting artificial intelligence tools to manage its citizen services, but more men than women say they are comfortable with the technological shift, according to a new survey. In a report published by Accenture, one-third of men said they trusted AI to manage their health care, while only 20 percent of women felt the same way. More than 40 percent of men trusted artificial intelligence to handle their taxes, only 34 percent of women felt the same way. The survey also revealed a significant generational gap in people's trust in artificial intelligence.

Verily developing AI-based heart disease test


Verily and its sister company Google Research are developing an AI-powered test for heart disease that analyses retinal imagery. "Our results indicate that deep learning of retinal fundus images alone can predict multiple cardiovascular risk factors, including as age, gender, and systolic blood pressure," write the study authors. "That these risk factors are core components used in multiple cardiovascular risk calculators indicates that our model can potentially predict cardiovascular risk directly." A significantly larger dataset or a population with more cardiovascular events may enable more accurate deep learning models to be trained and evaluated with high confidence."

4 Promising Ways AI Is Helping Diversity In Recruitment


Here's why: knowing AI has the potential to replicate an existing bias means we can monitor for it. AI promises to help us avoid unconscious bias during candidate screening. AI can be programmed to avoid these types of biases by ignoring this information when screening resumes. The flip side of AI ignoring demographic information is that AI can be used to find diverse candidates.

Men are better at Scrabble than women

Daily Mail

Men spent much more time analysing past games and practising anagrams - which gave them the winning edge in tournaments. 'The National Tournament divides players into six ranked divisions, and males dominate at the highest levels of performance', researchers wrote in the paper published in Psychological Research. 'In 2002, 86 per cent of competitors in the division with the best Scrabble players were male, while in the division with the lowest performance only 31 per cent of competitors were male. Men spent much more time analysing past games and practising anagrams - which gave them the winning edge in tournaments.

Unlearning Racism and Sexism in Learning Machines


"Themis can identify bias in software, whether that bias is intentional or unintentional, and can be applied to software that relies on machine learning, which can inject biases from data without the developers' knowledge." "When you learn from biased data, you are producing biased data, you are producing biased decisions. For example, Themis found that a decision tree-based machine learning approach specifically designed not to discriminate against gender was actually discriminating more than 11 percent of the time. But in the end, the data taught an algorithm to make, in effect, discriminatory decisions based on race.

Big Data will be biased, if we let it


And since we're on the car insurance subject, minorities pay morefor car insurance than white people in similarly risky neighborhoods. If we don't put in place reliable, actionable, and accessible solutions to approach bias in data science, these type of usually unintentional discrimination will become more and more normal, opposing a society and institutions that on the human side are trying their best to evolve past bias, and move forward in history as a global community. Last but definitely not least, there's a specific bias and discrimination section, preventing organizations from using data which might promote bias such as race, gender, religious or political beliefs, health status, and more, to make automated decisions (except some verified exceptions). It's time to make that training broader, and teach all people involved about the ways their decisions while building tools may affect minorities, and accompany that with the relevant technical knowledge to prevent it from happening.