Erasing Gender and Racial Bias With AI

C O N T E N T S:

KEY TOPICS

  • Hiring bias can be as serious as rejecting (or picking) someone because they?re a particular race or gender, but it can also be something as innocuous as picking someone for an interview because they went to the same high school you did.(More…)
  • That would cause the resulting model to disproportionately reject women (who still make up the majority of stay-at-home parents), even if gender isn’t one of the characteristics in its training dataset.(More…)
  • From what I understand, in nearly all cases the algorithms that make decisions about routine stuff don’t even have access to information about the person’s race, nationality, gender, etc. If so, how is bias even possible?(More…)

POSSIBLY USEFUL

  • Chowdhury, who showcased the tool publicly for the first time Tuesday at an AI conference in London, said Accenture uses a technique called mutual information that essentially eliminates the bias in algorithms.(More…)

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Erasing Gender and Racial Bias With AI
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KEY TOPICS

Hiring bias can be as serious as rejecting (or picking) someone because they?re a particular race or gender, but it can also be something as innocuous as picking someone for an interview because they went to the same high school you did. [1] That there’s still so much racial bias (unconscious or otherwise) in hiring is incredibly alarming–but it’s fixable. [1]

That would cause the resulting model to disproportionately reject women (who still make up the majority of stay-at-home parents), even if gender isn’t one of the characteristics in its training dataset. [2] For an image of a tree frog, LIME found that erasing parts of the frog’s face made it much harder for the Inception Network to identify the image, showing that much of the original classification decision was based on the frog’s face. [2]

From what I understand, in nearly all cases the algorithms that make decisions about routine stuff don’t even have access to information about the person’s race, nationality, gender, etc. If so, how is bias even possible? It sounds like the individuals it disfavors may have some kind of adverse event in their history that was fed into the algorithm. [3] You will also need to look at this and say, hey, this is actually a weird bias for a particular gender or race. [4] If you?re worried about bias creeping in, you can set the program to exclude metrics that indicate age, race, or gender. [5] Though you may not be aware of it, a job listing’s wording creates implicit gender bias. [6]

As you might expect, the historical data reflects the racial bias of previous generations, with a ProPublica study finding that COMPAS predicts black defendants will have higher risks of recidivism than they actually do. (The producers of COMPAS, Nothpointe Inc., disputes this analysis ). [7]

Big data algorithms wouldn’t use race and gender to filter applications because race and gender are not informative compared to grades, degrees, and other accomplishments. [8] Sure, you can keep up the fantasy of leaving, say, gender and race out, but they can easily be substituted by data that is the very target of these algorithms. [8] This becomes especially true if algorithms are trained on datasets that carry inherent human biases on such characteristics as gender, race, and social class. [9] Can you be certain that nothing in that dataset would identify race or gender? Even course history could betray this data. [8] Teams find more diverse candidates when race, gender and class-identifying information (names, university, personal activities, etc.) are removed via software like ours at untapt. [6] A consequence of not using race and gender to filter applications is precisely that the typical engineer in the U.S. ends up white and male. [8] We need to teach computers to lie to themselves about reality to cover for shortcomings of certain age groups, backgrounds, genders, and races. [8] “The motives behind unfair discrimination are generally defined at the highest level in the body of law; constitutions of countries prohibit discrimination on the basis of gender, race, beliefs, sexual orientation etc,” says Maestre. [10]

One of the things are the trivial way where, for instance, let’s use gender or age. [4] If people aren’t comfortable with a gender imbalance in their chosen career, they’re not going to be happy. [3] Your sexual orientation, your political affiliation?obviously things like gender and age. [4]

Others have raised the point that ML, when not supplied with racial information, might begin to redline certain neighborhoods where people of minorities tend to live. [3]

People have asked me about the gender and racial diversity of Mars. [11] How does racial bias come through in the algorithm? To a large extent, the AFST is simply mirroring unfairness built into the old human system. [12] If a correlation of dark skin and criminality is reflected in data based on patterns of racial profiling, then processing historical data will predict that blacks will commit more crimes, even if neither race nor a proxy for race is encoded as an input variable. [12]

Continue the example you have lived at MIT. Continue to engage with people outside your discipline, your gender, your race. [13] Businesses would be required to disclose the categories of information they have on users — including home addresses, employment information and characteristics such as race and gender. [14]

The reality is: algorithms that are gender biased or racially biased are not GDPR compliant. [15] Fight for paid family leave — with equal time for all genders — because equality in the workplace will not happen until we have equality in the home and because no one should be forced to choose between the job they need and the family they love. [13] Gender is absolutely an instance of binary, just like virtually everything in the universe. [16] Blurring boundaries and proliferating ways of approaching reproductive issues is a political project that loosens hegemonic epistemologies and institutional justifications, opening up possibilities for greater self-determination on a number of fronts, including gender, sex, sexuality, motherhood, parenthood, and the lived experience of health. [17] The idea behind such laws is to stop perpetuating historic discriminatory pay practices based on gender, race and ethnicity. [18] There will likely be significant diversity in terms of say, race, gender, sexuality, what have you – but almost no diversity on values. [11]

POSSIBLY USEFUL

Chowdhury, who showcased the tool publicly for the first time Tuesday at an AI conference in London, said Accenture uses a technique called mutual information that essentially eliminates the bias in algorithms. [19] While explainable AI and feature attribution for neural nets are promising developments, eliminating bias in AI ultimately comes down to one thing: data. [2] In order to develop fair and accountable AI, technologists need the help of sociologists, psychologists, anthropologists, and other experts who can offer insight into the ways bias affects human lives and what we can do to ensure that bias does not make ML-enabled systems harmful. [2] Inviting AI into the process just means doing a different kind of prep work to help standardize the process and eliminate as much personal bias as possible. [1] While AI may replace some of the agency that you currently have in the hiring process, it helps make important strides toward saving us from our unconscious hiring bias. [1] Short on time? Here’s an infographic on how AI eliminates hiring bias. [1]

“Our clients are telling us they are not equipped to thinking about the economic, social and political outcomes of their algorithms and are coming to us for help with checks and balances,” Rumman Chowdhury, a data scientist who leads an area of Accenture’s business called Responsible AI. [19] Because AI screening and recruitment programs are based on data, you have the power to control what data the program is crunching. [1] Explainable AI asks ML algorithms to justify their decision-making in a similar way. [2] AI recruiting is a powerful tool that can feel a bit threatening (“am I being replaced by a robot?”) but in reality, it gives HR professionals a major asset in streamlining the hiring process to make it more fair and merit-based. [1] It’s not just the initial candidate screening and resume-reading phases where AI programs can help improve the process. [1] Many companies are using AI to make their interview processes more fair and effective. [1] Artificial intelligence, or AI, has become a significant force in the HR world in the past few years. [1]

If you?re concerned about hiring bias (unconscious or otherwise) entering the process, you can set the program to exclude data and metrics that indicate things like age, race, sex, or location. [1] Instead of excluding criteria that is rife with recruitment bias, the data can also be used to make sure that your hiring process meets Equal Employment Opportunity Commission (EEOC) guidelines. [1]

If the data an algorithm is trained on doesn’t fairly reflect the entire population that developers want to serve, bias is likely to occur. [2] If human operators could check in on the “reasoning” an algorithm used to make decisions about members of high-risk groups, they might be able to correct for bias before it has a serious impact. [2] Reducing bias in machine-learning algorithms doesn’t just require advances in artificial intelligence it also requires advances in our understanding of human diversity. [2] Algorithms for solving this task have been embroiled in controversies over bias before. [2] The ML-enabled system thus ends up amplifying existing human historical bias. [2] All of this can be done without the human reviewer/interviewer knowing whether a candidate falls into a particular bias zone. [1] Discussions of bias are often oversimplified to terms like “racist algorithms.” [2] Algorithmic developments help, but the obligation to overcome bias lies with the designers and operators of these decision-making systems, not with the mathematical structures, software, or hardware. [2] The increasing prevalence of machine learning-enabled systems introduces a host of issues, including one with an impact on society that could be huge but remains unquantifiable: bias. [2] These tiny calculations often happen in our heads without us realizing what’s going on, and before you know it, bias has crept into the process. [1]

Microsoft is developing a tool that can detect bias in artificial intelligence algorithms with the goal of helping businesses use AI without running the risk of discriminating against certain people. [3] Eliminating Bias from AI means discarding facts and data that violate SJW principals. [3] At the Re-Work Deep Learning Summit in Boston this week, Gabriele Fariello, a Harvard instructor in machine learning and chief information officer at the University of Rhode Island, said that there are “significant. problems” in the AI field’s treatment of ethics and bias today. [3] Artificial intelligence (AI) can eliminate unwanted bias during the hiring process. [5] The danger is that perceived “racism” will be corrected with “affirmative action”: by verifying the AI using statistics on the outcome, and applying a bias. [8] The authors of the ProPublica article are no longer with the organization, but this article shows up in any news article about AI bias. [3] The whole point of AI categorization systems is to uncover bias. [3]

It may not even be?and I think this is the warning that I would put out there?you don’t have to have the data of your customer records for AI to infer it and use it like it’s true, and it probably will be true. [4] The AI can look at completely innocent data and infer all sorts of stuff you don’t actually want to infer and use during customer engagement, so that’s the other thing. [4] AI uses existing data to make predictions, such as taking logs of previous service centre calls to automatically predict the correct response to give to customer questions. [7] These are just a few examples of how we are changing the way we do business and differentiate ourselves from the competition via the use of big data and AI. [9] What if the AI applies such strictly relevant data to approve or reject loan applicants, but the rejected group happens to be predominantly of a certain race? Verification of the AI will show a (non causal) relation between race and loan applicant score, and since we don’t know how the AI arrived at its decision, people will assume racism. [8] Not just by data scientists and people with PhD in AI and that kind of profile. [4] For instance, maybe you don’t have rates in your data, but for some reason, your opaque AI is inferring that from all sorts of other stuff and has unsavory algorithm or strategy. [4] In this article, BBVA data scientists construct a dynamic price-setting process using an AI model that incorporates “principles of justice based on fairness” to avoid discrimination. [10] We need to be vigilant in training AI to ensure our input data doesn?t contain conscious or unconscious biases, or else we risk a feedback loop of prejudices, with the skewed output of one model feeding another, each iteration predicting a further distortion of reality. [7] MY: A lot of the of the AI comes from the really rich customer data that you have and that your customers have. [4] I think GDPR is making a lot of difference, especially in the conception around what AI can do and data, right, so it’s asking a lot of good questions. [4] Even if you have very limited data, with the sort of advanced AI that we have now, it’s still possible to infer a lot of things. [4] I have been fortunate to have had multiple female mentors who have shown me how to be a successful as a female in traditionally male-dominated fields like AI, data science, and engineering. [9] AI works off data, meaning you still have the power to crunch the numbers. [5] There are many definitions of AI, from machines that work and think like humans, through to “any device that perceives its environment and takes actions that maximise its change of successfully achieving its goal”. [7] These biases can skew the results from an AI algorithm, predicting not the “truth? of a situation, but the outcome a biased human would make. [7] Can we be confident our AI is working in a way we are comfortable with? We must be extremely cautious with AI that our algorithms are using only relevant features, applying techniques such as LIME to open up more of the black box. [7] We’re trying to decide our next best action, so that’s basically using a lot of AI to get us to the evidence and the insights, and then we use economic decisions, business decisions to finally prioritize what’s being done. [4] We try to use a lot of AI to make what we call “next best action” decisions–that’s where all the AI comes in–and at the same time, we then also have automation, process automation, to make good on all of those decisions. [4]

The summary and most articles these days use “algorithm” and “AI” interchangeably. [3] We put a lot of effort into making that simpler, to make sure that business can be in control, and marketers can be in control, and risk managers can be in control, and then still have control over their AI algorithms. [4] In the United States, there are different associations that bring together the university campus with civil activism such as AI Now in which New York University and the Algorithmic Justice League with the help of MIT Media Lab raising their voice to warn about the power of algorithms. [10] Beyond our products, we’re using AI to help people tackle urgent problems. [20] One of the problems facing these scientists when assessing the transparency of AI is the known dilemma of the “black box? which often makes it virtually impossible to know the path taken by an AI model to arrive at a certain conclusion or decision. [10] I think on the optimization front, I think where this is going, and I think where AI will become a big part of that, is that instead of determining relevance and optimizing decisions, I think the name of the game will become optimizing the actual business model. [4] Even if you don’t use particularly advanced AI but just more simple predictive models, I think that is a very good practice to follow. [4] Some of that may be important, and I think AI can really help determining what you actually shouldn’t be looking at, what doesn’t matter enough to make the investment or risk being out of compliance. [4] The other thing is about the compromise that you mention, and especially because we’re pretty hot on AI, we think that’s a really important thing, and it drives a lot of the return on these kind of things. [4] Now, GDPR is a really big deal now, and I’m really curious, how has this new regulation affected how you build AI systems, especially because these AI systems can infer a lot of information about customers that they did not voluntarily give you. [4] Swatee: We are focused on improving every aspect of the customer experience through AI. The size and scale of American Express? systems and applications, and especially our industry-leading position in big data, have allowed us to get to the market earlier than most financial services companies with AI- and robotics-enabled solutions for customers. [9] In her recently published 2018 trends report, Mary Meeker discussed how the combination of accelerated data gathering due to computer adoption and the declining cost of cloud computing, have enabled Artificial Intelligence (AI) to emerge as a service platform. [7] Lack of cohesive laws and regulatory-legal preparedness: From a regulatory perspective, our archaic laws and methods of enforcement haven?t yet caught up with the potential for the misuse of data in an AI world. [9] RW: So in communications, we typically follow the money and churn is a big thing, but then, initially, after that sort of success improves AI, it increases our confidence/competence factor, not just for the company, but also the people that are touched by the AI. Like for instance agents, or people in the retail shops, they have to trust these sort of AI recommendations. [4] MY: Speaking of being practical for businesses, can you give us an overview of what it is you do at Pega, and how you’re using AI to drive business ROI for the company and your customers. [4] With all of those sorts of insights, AI was being used, and then, when a customer that was likely to churn, we would proactively decide on the next best action and try to convince them–again, with AI–to stay with that company. [4] In their case, initially, AI and business rules and decisioning was used to look at a particular customer, decide on the risk of that customer leaving in the near future, but also to calculate the budget for retaining that customer, to make sure that we right-sized the effort. [4] Autonomous machines that are powered by AI will become another customer for financial services companies like ours, and this will transform and bring new forms of value and interaction to this industry. [9] You?ve screened your candidate using AI; now it’s your turn! AI programs like Ansaro can allow you and your team to collaborate on proper, non-biased interview questions to ask against a grading system that you create. [5] You can only do that if a lot of that is centralized and a lot of that is made explicit, but then you can have AI that would say things like, “Oh, if you did a little more of that and a little less of this, and if you put it on that stream and spend more budget here, actually these AI would go up. [4] Most AI movies depict anthropomorphized bots, but I feel it’s a lot more likely that what we actually will experience is along the lines of a smarter washing machine, smarter homes or even smarter water utilization, as water becomes an increasingly precious resource. [9] In true science fiction AI manner it will conclude that the only way to reconcile this dilemma is to destroy the lot of them. [8] Without noticing, in one way or another, AI has become a part of our daily lives, and this may be just a hint of what’s to come in the future. [10] AI algorithms and datasets can reflect, reinforce, or reduce unfair biases. [20] As with every powerful new technology, AI brings with it a tremendous opportunity for businesses and individuals alike; but it also brings risks, from well-intentioned but poorly chosen training data to algorithms powered by correlation instead of causation, to our ultimate peril: an abdication of our ethical duty. [7] Whilst the specifics of the definition are still discussed, what is now unequivocal is the power AI has to shape our lives, with intelligent assistants such as Siri and Alexa understanding our spoken commands, predictive maintenance algorithms that can optimise equipment repair, and cars that can drive themselves. [7] Machine learning, a sub-field of AI, contains a diverse set of models to create predictions, such as a person’s credit score, or classifying an object into a category, for example, if an image contains a human face or not. [7] Our AI technologies will be subject to appropriate human direction and control. [20] I think most of these aspects, AI is a really?it is a bit of a challenge that we can control, and we explain that, but we can control it. [4] If it carries a legal significance, there is a requirement for companies to be able to explain that, and that puts a burden on AI, that I think is a double-edged sword. [4] At the time, I think AI was still trying to beat Gary Kasparov, maybe. [4] It’s arguable that it is too much to expect hard rules along these lines on such short notice, but I would argue that it is not in fact short notice; Google has been a leader in AI for years and has had a great deal of time to establish more than principles. [21] We want to be clear that while we are not developing AI for use in weapons, we will continue our work with governments and the military in many other areas. [20] Doctors are starting to use AI to help diagnose cancer and prevent blindness. [20] One of the things that we would typically do is, first of all, we would use AI to “follow the money”, not our money but the money for our customers. [4] I began my journey in this space looking to use AI to detect breast cancer. [9] Swatee: As we look to use more and more open source tech stacks to power our AI revolution, the most fun we have had is in creating chatbots from scratch for internal use. [9] As practitioners in digital and marketing, we all have a tremendous opportunity to reap the benefits of AI, and an even greater obligation to ensure its ethical use. [7] We will incorporate our privacy principles in the development and use of our AI technologies. [20] There’s also another side to that, which we may want to talk about a little later, but challenge here is that AI is very capable of inferring stuff that may not be politically correct, but then you can use it in your marketing or in your risk management strategies, and you may not even be aware of it. [4] Artificial intelligence (AI) is often defined as a field of computing that creates systems able to perform tasks normally requiring human intelligence such as translating documents, driving a car and recognizing faces. [10] Uh not unless it’s a really crappy AI. If you haven’t noticed, chances are any human directive will be treated as that by the neural network – another signal that is larger/more salient because it is input by a human. [3] Some employees had opposed the work and even quit in protest, but really the issue was a microcosm for anxiety regarding AI at large and how it can and should be employed. [21] This is basically saying that MS is trying to create tools to make AI that doesn’t work. [3] We aspire to high standards of scientific excellence as we work to progress AI development. [20] As AI technologies progress, we’ll work with a range of stakeholders to promote thoughtful leadership in this area, drawing on scientifically rigorous and multidisciplinary approaches. [20] Maybe the AI has figured out that it’s because you just are not always connected?you have dropped calls and it’s very annoying, especially because you have them at home or at work, places you frequently are at and making calls, or maybe it’s price in your particular case. [4] It made me realize the power of what AI can do to improve care and extend the lives of people. [9] In this ever increasingly connected world, AI has the potential to uplift the lives of millions of people across the globe. [9] Last month, Pichai unveiled a new technology called Google Duplex, a stunningly realistic-sounding AI that can book dinner and salon reservations for people over the phone. [20] We will strive to make high-quality and accurate information readily available using AI, while continuing to respect cultural, social, and legal norms in the countries where we operate. [20] Challenges that GDPR poses to enterprises using AI & machine learning. [4] Through AI, we can predict their needs and enhance their customer experience by making it easier for them to choose the right card product. [9] Swatee: The very core of American Express is a commitment to service, and everything AI touches in this company directly improves the lives of our customers. [9] The ethics of AI has become a hot button issue that has roiled the company recently. [20] We believe these principles are the right foundation for our company and the future development of AI. This approach is consistent with the values laid out in our original Founders’ Letter back in 2004. [20] RW: We believe that, first of all, for AI to really proliferate, it needs to be accessible. [4] We need to talk about artificial intelligence (AI), and not because of the hype surrounding it, but because it is all around us. [10] It is necessary to integrate specific measures to control possible biases originated by the AI itself. [10] Fortunately, we have controls as an industry against them, but enforcing these controls may become a herculean task in an AI reality. [9] Such questions acquire increasing importance in a context in which AI is beginning to form part of the day-to-day existence of companies and individuals. [10] Even as AI creeps deeper and deeper into our everyday lives, serious questions remain about artificial responsibility. [7] As the scope for AI continues to grow, so does the call for legislation and oversight, with the question shifting from what’s possible, to what’s ethical. [7] As part of our AI For Growth executive education series, we interview top executives at leading global companies who have successfully applied AI to grow their enterprises. [4] Google has published a set of fuzzy but otherwise admirable “AI principles” explaining the ways it will and won’t deploy its considerable clout in the domain. [21] How AI is developed and used will have a significant impact on society for many years to come,” Google CEO Sundar Pichai said in a blog post Thursday. [20] Can you say the same for the rest of your team? This is where AI can jump in to help. [5] AI will help us move toward a Knowledge Society?–?one that promotes equal, inclusive, and universal access to all knowledge creation. [9] We also take this one step further; our AI software can help automatically screen candidates for you, removing unqualified applicants and ensuring overall quality in each resume you review. [6] RW: ?But I did my PhD in the 90s, so that clearly dates me a lot, but even then, AI was incredibly attractive. [4] RW: The thing is, and this is actually the trick, this is where AI comes in. [4] I’m responsible for that space, what we do (in summary) around AI, essentially, is we try to optimize customer moments. [4] MY: We’ve already talked about how AI can make systems more difficult to build because they infer so many features about your customers they may not explicitly tell you. [4] With an AI program, you can easily enter EEOC values to process applications. [5]

The ideas here are praiseworthy, but AI’s applications are not abstract; these systems are being used today to determine deployments of police forces, or choose a rate for home loans, or analyze medical data. [21]

An algorithm that uses historic data, which was distorted by human bias, to predict future events. [3] If the DATA is scrubbed of bias, then the ONLY thing the algorithm can base it’s decision on is individual behaviour. [3] Most cases of “bias” by machine learning, which is really just branch of statistical analysis, systems tends to be the developers intentionally skewing the result or then, in the vast majority of cases, population level differences that cause something that appears to be racist if you don’t understand the data the system is making decisions based on or how it actually makes those decisions. [8] Where it becomes more difficult is if you are looking for bias on data or an outcome that you actually do not have a record. [4] As long as the data is correct and not forged with an inherent bias, then the findings are valid. [3] Imagine an algorithm to roll a six-sided dice, and we define bias as anything where a given number appears more than 1/6 of the time on average, and a tool to detect bias works by running the algorithm a lot and checking frequencies. [3] No there’s no way this tool could be used to insert bias into algorithms without detection, by definition. [3] We have judges using the COMPAS Recidivism Algorithm to help determine sentences despite evidence pointing toward a bias against black defendants. [8] Bias: Underwriting and decisions on loans are now increasingly made using automated AI-powered algorithms. [9] If both prior convictions and the measure of recidivism are biased, the algorithm will correctly use the prior bias to predict the future bias. [3] If a certain group doesn’t like the findings, maybe they should figure out how to address the underlying causes and not call an accurate analysis “bias” or “discriminatory” or whatever other term they want to use because their feelings got hurt. [3] “Things like transparency, intelligibility, and explanation are new enough to the field that few of us have sufficient experience to know everything we should look for and all the ways that bias might lurk in our models,” he told MIT Technology Review. [3] We are providing the tools to set up the bias test in whatever way it’s acceptable and part of compliance that you need to be under. [4] Correctly read as: “Microsoft is developing a tool to help developers detect wrong bias in their algorithms.” [3] Bias in algorithms is an issue increasingly coming to the fore. [3] If there was bias in how the subject was convicted in earlier cases, then the algorithm will codify that bias. [3] If the algorithm wants to be fair and avoid perpetrating that bias, it is going to have to examine each case in great detail. [3] If you insinuate those factors into your classification algorithm then you have exactly an institutionalized bias. [8] I’ve been reading stories in removing bias from algorithms but still don’t get it. [3] The algorithm comes into this system as it is, full of existing systemic bias. [3] That’s because calling it “algorithm bias” is a category error. [3] It’s easier to test them for bias than humans, and decisions are made in a consistent, repeatable manner. [3] Hiring bias is, unfortunately, a real product of the human condition. [5] Hopefully you found these tips on how to eliminate hiring bias helpful. [6] People are naturally prone to bias in many different forms, and even HR experts are no exception. [6] Who decides what is biased or not? That depends on the type of bias being examined. [10] The main problem with this endeavor is that the “bias” they are trying to suppress is actually the opposite of bias. [3] “And depending on the problem, determining whether a bias is positive or negative is no easy matter since it depends on the point of view of the person you ask”. [10] MY: How does a company create a bias test? Because there’s a lot of controversy around what even is bias. [4] Having a diverse group looking through resumes and speaking to candidates not only reduces their overall bias, but also gives minority candidates a more positive outlook on the company during interviews. [6] The trained model can definitely have bias based on the training data. [3] Even this simple model shows the same “bias” that COMPAS is accused of. [3] Still, COMPASS shows a strong bias, as it overestimates the recidivism of black people by a factor of two while at the same time, underestimates the recidivism rates of whites by about the same amount. [3] What’s statistical bias versus a social bias? What counts as unfair or fair? It’s a loaded question. [4] Methods for increasing transparency and control of algorithmic processes while reducing bias. [4] For instance, we try to make it easy and having these bias tests as part of our quality assurance methodology. [4] It all depends on what they mean by “bias” and what kind of tool they’re writing. [3]

When using AI to drive human resources strategy, HR professionals must monitor systems for bias. [18] The final potential area for non-compliance is the inherent bias when AI services only a small number of languages, or perhaps only one. [15]

In the HR context, AI typically refers to data that is processed by algorithms to make decisions, he explained at the California State Council of the Society for Human Resource Management 2018 California State Legislative &HR Conference. [18] Forty-nine percent of respondents to law firm Littler’s 2018 Annual Employer Survey said they use AI and advanced data analytics for recruiting and hiring. [18] Nationwide unemployment rates are low and technology can help employers find the best talent in a tight market–but HR can use artificial intelligence (AI) in a number of ways beyond hiring. [18] Sure, you can set some parameters to the way the AI interacts with the data, but the moment you turn it on, thats the end – you cannot control it anymore, you cannot input any other bullshit into it anymore etc. [16] Facebook uses AI algorithms to discern the mental and emotional states of its users. [12] This is exactly why IF an AI manages to become sentient, it will definitely wipe out the entirety of the human race – were so far gone that it would make the most sense to destroy us completely. [16] This AI bot has wrongly predicted that a horse will beat a hedgehog around a horse track based on flawed information and human mischief. [16] AI and automation could radically change the workplace and human resource management, said Alden Parker, an attorney with Fisher Phillips in Sacramento. [18] Ensuring that AI can be used by everyone equally has always been the greatest passion in my work. [15] AI technologies that we took for granted 20 years ago still don?t work in those languages today. [15] Those are NWO sources of power so wont get tackled, they believe that AI will solve every problem, and make them into imperial gods. [16] Go and have a think about how AI can digest the “social context” without being a half-mute lobotomised creature. [16] I more clearly remember Jia Deng, now a professor at the University of Michigan, first presenting it to an internal AI group at Stanford in 2009 when we were both Ph. students there, and being really impressed. [15] The really scary thing is, to make an AI regurgitate their leftist beliefs, they will quite literally have to make it insane/brain-damaged. [16] This REALLY is the ultimate red pill, AI will be in everything by 2020. [16] AIs are innately racist because race exists and AI are objective. [16] For the HR function, AI is most commonly used for talent acquisition. [18] Reinforcement Learning Notebooks — there’s also a good selection of other Jupyter AI notebooks in the Hacker News comments. [14] Think about a sort of AI that will act as a curator for the planet, keeping it in perfect balance so that life will never end. [16] I fucking cannot wait for an “AI” developed by feminists that live in the multicultural paradise of London, just so I can laugh in their faces because the end results will literally be the same. [16]

This close to real A.I. and these people keep projecting bullshit that doesnt work upon it. [16]

The “problem” is that the lefties dont like it, and are themselves biased, so as far as theyre concerned, the data is “wrong” and the unfeeling robot has a bias, not them. [16] The real problem is the bias towards algorithms in academia and how this has dominated the debate. [15] Despite this real-world experience, an academic bias has dominated the debate: people have only worried about algorithms. [15] Implicit Bias ? An Implicit Bias is a preference for OR against a person or group of people that operate at the subconscious level. [22] Compass Health Unconscious and Implicit Bias Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. [22] Even if throwing out the First Amendment doesn’t appall you, it wouldn’t actually address the problem of implicit bias. [12] Humans tend to operate with something called a status quo bias – an emotional preference for whatever already exists, no matter its quality. [11] We know that there are implicit and unconscious bias that gets in the way of our acting in congruence with our values. [22] We need to make a personal commitment to stop racism and sexism, including the expressions of bias that become commonplace and accepted instead of rejected and fought. [13]

RANKED SELECTED SOURCES(22 source documents arranged by frequency of occurrence in the above report)

1. (42) Driving Customer Engagement and Transparency With AI (Interview with Rob Walker of Pega) – TOPBOTS

2. (32) Microsoft Developing a Tool To Help Engineers Catch Bias in Algorithms – Slashdot

3. (16) At the Core of Commitment: How American Expresses Utilizes AI to Prioritize Customer Care

4. (14) Goodbye Hiring Bias! How AI Filters Applicants Fairly

5. (13) /pol/ – Norman, the psychopathic AI – Politically Incorrect – 4chan

6. (13) Explainable AI could reduce the impact of biased algorithms | VentureBeat

7. (13) Read Google’s AI ethics memo: ‘We are not developing AI for use in weapons’ – CNET

8. (13) The perils of artificial responsibility: Developing and applying AI

9. (11) How to make artificial intelligence more ethical and transparent | BBVA

10. (11) New Toronto Declaration Calls On Algorithms To Respect Human Rights – Slashdot

11. (7) 3 Ways GDPR is More Important for Machine Learning Than You Think

12. (7) How Can Artificial Intelligence Work for HR?

13. (7) How AI programs can overcome hiring bias – TheJobNetwork

14. (6) 5 Ways to Eliminate Hiring Bias from your Next Search. – untapt blog

15. (4) The Digital Poorhouse | by Jacob Weisberg | The New York Review of Books

16. (4) Google’s new ‘AI principles’ forbid its use in weapons and human rights violations TechCrunch

17. (3) In The Mesh Magazine – Frontier Mars: An Interview with Michael Solana

18. (3) Sheryl Sandberg’s Commencement address | MIT News

19. (3) Compass Health Unconscious and Implicit Bias

20. (2) – O’Reilly Media

21. (2) Accenture Offers Way to Erase Gender, Racial, Ethnic Bias in Artificial Intelligence

22. (1) (Anti)Institutional Menses: Our Blood, Our Business | Somatosphere

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