(testing signal)

Tag: bias

Explainable AI is about to become mainstream: The AI audits are here – Impact of AI recruitment bias audit in New York…

A few weeks ago, I said that we will be increasingly faced with AI audits and that I hoped such regulation would be pragmatic.  (Could AI audits end up like GDPR).
That post proved prophetic
The New York city council has passed a new bill which requires mandatory yearly audits against bias on race or gender for users of automated AI based hiring tools
Candidates can ask for an explanation or a human review
‘AI’ includes all technologies – from decision trees to neural networks
The regulation is needed and already, there is discussion about adding ageism and disabilities to this…

Want to develop ethical AI? Then we need more African voices

Artificial intelligence (AI) was once the stuff of science fiction. But it’s becoming widespread. It is used in mobile phone technology and motor vehicles. It powers tools for agriculture and healthcare.But concerns have emerged about the accountability of AI and related technologies like machine learning. In December 2020 a computer scientist, Timnit Gebru, was fired from Google’s Ethical AI team. She had previously raised the alarm about the social effects of bias in AI technologies. For instance, in a 2018 paper Gebru and another researcher, Joy Buolamwini, had shown how facial…

Why AI needs input from Africans — Quartz Africa

Artificial intelligence (AI) was once the stuff of science fiction. But it’s becoming widespread. It is used in mobile phone technology and motor vehicles. It powers tools for agriculture and healthcare.But concerns have emerged about the accountability of AI and related technologies like machine learning. In December 2020 a computer scientist, Timnit Gebru, was fired from Google’s Ethical AI team. She had previously raised the alarm about the social effects of bias in AI technologies.For instance, in a 2018 paper Gebru and another researcher, Joy Buolamwini, had showed how facial…

Defining what’s ethical in artificial intelligence needs input from Africans

Artificial intelligence (AI) was once the stuff of science fiction. But it’s becoming widespread. It is used in mobile phone technology and motor vehicles. It powers tools for agriculture and healthcare.

But concerns have emerged about the accountability of AI and related technologies like machine learning. In December 2020 a computer scientist, Timnit Gebru, was fired from Google’s Ethical AI team. She had previously raised the alarm about the social effects of bias in AI technologies. For instance, in a 2018 paper Gebru and another researcher, Joy Buolamwini, had showed how…

8 Ways Older Workers Can Benefit Your Organisation

Image via: Unsplash

As life expectancy rates climb to greater heights than ever before, more experienced individuals are considering staying in the workforce for longer. However, this can be made considerably harder by the prevalence of ageism in the workforce.
With discrimination being perpetrated consciously or through unconscious bias, older workers are being confronted by ageism at an alarming rate. This is especially disappointing when you consider that most of these biases are based on misconceptions. 
At the end of the day, when an organisation allows ageism to permeate through…

Ethereum, Shiba Inu, VeChain Price Analysis: 18 November

After a bullish rally in October and early November, last week saw a significant correction for most cryptos.
Ethereum and Vechain registered double-digit losses over the past week. On the other hand, Shiba Inu has been on a steady downtrend for the past three weeks. Accordingly, near-term technicals for all the aforestated cryptos undeniably reflect a bearish bias.
Ethereum (ETH)
Source: TradingView, ETH/USDT
Ether heightened in a slope between two parallel channels over the past seven weeks. The largest altcoin performed well after registering a 61% ROI from 22 September to 10 November….

How This Delhi-based Startup Is Building A Time Machine For AI

With the rapid adoption of AI across enterprises, the need for labelled data has increased significantly over the last few years. However, many companies are still using manual annotation, even to this day. This leads to human bias, affecting the model accuracy. Studies show that 85 per cent of machine learning (ML) and artificial intelligence (AI) projects fail or do not progress beyond minimal viable product (MVP) due to the quality and quantity of labelled data. 
This is where New Delhi and Palo Alto-based DataNeuron comes into play. The company helps in accelerating and automating…

Council Post: Building Human-In-The-Loop Systems

The increasing performance and integration of AI in the workplace have been accompanied by increasing complexity in the models. Models, by definition, are a subset of the real world. This means that there will be some relevant external context that is not a part of the model—this truth is expandable to AI models as well. 
Human-in-the-loop systems are essentially about providing this context to AI models. This context could be in various terms. It includes removing bias from models so they adhere to ethical standards, providing situational awareness information to improve predictions…

Do You Want To Deploy Responsible AI In Your Organization? Join This Session To Operationalize…

As AI adoption increases across industries, the emphasis has shifted heavily to developing and deploying ethical, responsible AI applications.
Responsible Artificial Intelligence is a positive force. According to Gartner, Responsible AI encompasses several aspects of making the right decisions when adopting AI, aspects that are often addressed independently by organizations. A responsible AI framework focuses on bias detection, privacy, governance, and explainability to help organizations harness the power of AI.
Nevertheless, the practical implications of Responsible AI are unclear. Can…

Bias still dominates the discussion of AI adoption in business. So it should.

As international AI regulation starts to take shape, organisations must learn from the past, writes James Duez. In his blog post, he explains why predictive technology still requires human judgement, and offers three principles all AI-enabled organisations must live by.
SwissCognitive Guest Blogger: James Duez, Technologist, Futurist, Co-Founder & CEO, Rainbird Technologies Official Member Forbes Technology Council

As international AI regulation starts to take shape, organisations must educate themselves on lessons from the past. One thing is clear: implementing AI and automated…

Aussie crypto micro investment app Bamboo raises $3M, eyes US market


Blake Cassidy, CEO of Australian micro-investment app Bamboo, has claimed that the Australian Securities Exchange’s (ASX) bias against listing crypto companies is causing an Aussie brain drain leading firms to seek out a US listing.Cassidy’s comments come in the same week the company announced a $3 million ($4M AUD) Series A investment round including participation from Australia’s largest cryptocurrency hedge fund, Orthogonal Trading, Mountain Ash Investment Management, and VP…
https://cointelegraph.com/news/aussie-crypto-micro-investment-app-bamboo-raises-3m-eyes-us-market

The System Justification Bias

People feel safer with the stablished order in the face of potential change. That’s partly why people buy the same things they bought before, return to the same restaurants, and keep the same opinions.

This has been called the system jutification bias, and it has interesting paradoxical effects.  For instance; poor people don’t strongly support the political policies that would make them better off. Surveys show consistently that low-income groups are hardly more likely than high income groups to want taxes that mean they’ll get more money.

Oddly, the more disadvantaged people people are, the more they are likely to support a system that is doing them no favors.

In US, low income latinos trust more government than high income latinos.… Read more...

(Practical) AI ethics

PODCASTMargaret Mitchell on the biggest challenges in AI fairness and biashttps://medium.com/media/90e6495a6a8ceca2968c20c61d4d475f/hrefEditor’s note: The TDS Podcast is hosted by Jeremie Harris, who is the co-founder of SharpestMinds, a data science mentorship startup. Every week, Jeremie chats with researchers and business leaders at the forefront of the field to unpack the most pressing questions around data science, machine learning, and AI.Bias gets a bad rap in machine learning….

Do algorithms introduce bias or do they promote transparency?

Background
Every industry will be affected by algorithms and algorithmic bias. But we could rethink this as – Do algorithms introduce bias or do they introduce transparency?

An interesting paper illustrates this point. Assessing Algorithmic Biases for Musical Version Identification  is a study on algorithmic biases for musical version identification. In the music industry, the Version identification (VI) systems is the mechanism used to detect different renditions of a musical…

Why Trust Matters in AI

We can all agree that AI has the potential to help businesses, organizations, and society solve real problems. But there are still many concerns about the consequences of using AI improperly. Things like ethics, privacy, bias, and security are top of mind. In order for AI projects to be fully embraced, companies must address these concerns, because people need to trust their AI in order for projects to be successful. In this installment of our blog post series, we will explore the question of why trust is an essential component to any conversation around AI.

Performance, Operations, and Ethics

Trust in AI is multidimensional. AI creators, operators, and consumers all have different needs and different factors that they consider when evaluating and determining if an AI application is trustworthy.… Read more...

Cloudera Shines Educational Spotlight on Data and AI with Children’s Book for 8- to 12-year-olds

Cloudera, Inc. (NYSE: CLDR), the enterprise data cloud company, announced “A Fresh Squeeze on Data,” a downloadable children’s book that explains simple ways to problem solve with data in a manner that kids can understand. The book was created in partnership with education company ReadyAI, with the goal of making data and AI more interesting and accessible to 8- to 12-year-olds.

Available on Amazon, “A Fresh Squeeze on Data,” explains complex data concepts, including Machine Learning model training and data bias, in simple terms. The book was written by ReadyAI’s team of educators, who specialize in providing K-12 students with an inclusive approach to learning and advancing AI and technology concepts.

As part of this initiative, Cloudera and ReadyAI sponsored the Ulbrich Boys & Girls Club’s “Summer Brain Gain” summer camps in North Haven and Wallingford, Connecticut, where new programs like “homework buddy” for virtual learning were piloted along with “Summer Brain Gain” courses which add additional curriculum to aid educators.… Read more...

Towards Removing Gender Bias in Writing

 

Learn how to understand the amount of gender bias in your daily consumption of literature and avoid it in your own writing.

A yellow family sign painted on cement. From left to right: a daughter holding a father’s hand, a father, and a mother holding a baby.
Photo by Sandy Millar on Unsplash
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Control Variables

Control variables are properties that researchers hold constant for all observations in an experiment. While these variables are not the primary focus of the research, keeping their values consistent helps the study establish the true relationships between the independent and dependent variables. Control variables are different from control groups.

What is a Control Variable?

In science, researchers assess the effects that the independent variables have on the dependent variable. However, other variables can also affect the outcome. If the scientists do not control these other variables, they can distort the primary results of interest. In other words, left uncontrolled, those other factors become confounders that can bias the findings.

Read more...

Trust in Your Production Models with Bias Monitoring

Evaluating bias is an important part of developing a model. Deploying a model that’s biased can lead to unfair outcomes for individuals and repercussions for organizations. DataRobot offers robust tools to test if your models are behaving in a biased manner and diagnose the root cause of biased behavior. However, this is only part of the story. Just because your model was bias-free at the time of training doesn’t mean biased behavior won’t emerge over time. To that end, we’ve extended our Bias and Fairness capabilities to include bias monitoring in our MLOps platform. In this post, we’ll walk you through an example of how to use DataRobot to monitor a deployed model for biased behavior.

The Data

We’ll be using a dataset that contains job applications and training a model to predict if the candidates were hired or not.… Read more...

Modern Tube Tester Uses Arduino

There was a time when people like us might own a tube tester and even if you didn’t, you probably knew which drug store had a tube testing machine you could use for free. We aren’t sure that’s a testament to capitalistic ingenuity or an inditement of tube reliability — maybe both. As [Usagi] has been working on some tube-based projects, he decided he needed a tester so he built one. You can see the results in the video, below.

The tester only uses 24V, but for the projects he’s building, that’s close to the operation in the real circuits. He does have a traditional tube tester, but it uses 100s of volts which is a different operating regime.

The bulk of the circuit is creating the voltages required, including a 555 charge pump to generate around -10V.

Read more...

Machine Learning Model Selection strategy for Data Scientists and ML Engineers

“Thus learning is not possible without inductive bias, and now the question is how to c right bias. This is called model selection.” ETHEN ALPAYDIN (2004) p33 (Introduction to Machine Learning)

Really there are many more definitions concerning Model Selection. In this article, we are going to discuss Model Selection and its strategy for Data Scientists and Machine Learning Engineers.

An ML model(s) are always constructed using various mathematical frameworks and that would generate predictions based on the nature of the dataset and finding patterns out of it.

Most of them are really confused between two terminologies in machine learning – ML-Model and ML-Algorithm. Even me too. But over the period I got to understand the thin line between these two terms.… Read more...