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Hard Influential Computing Researchers and Practitioners Announce Stepsto PreventAlgorithmic Bias
U.S. and European Computing Researchers and Practitioners Announce Stepsto PreventAlgorithmic Bias
Iyad Rahwan , Society - in - the - Loop Influential Computing Researchers and Practitioners Announce Stepsto PreventAlgorithmic Bias
the order of the adjacency matrix(passive) caused bythe algorithmic bias
The following steps will helppreventalgorithmic bias
Buolamwini ... that “ who codes matters , ” as more diverse teams of programmers could helppreventalgorithmic bias
this less nuanced understanding of how Black patients may use the healthcare systemresultedin algorithmic bias
Potential solutionsto preventalgorithmic bias
One factorleadsto algorithmic bias
bad data(passive) caused byAlgorithmic bias
a broad , complete information set about households , which are harder to obtain , thus ... partial information setsmay leadto algorithmic bias
algorithms ... partial information setsmay leadto algorithmic bias
any attempt to reduce the bias introduced during the development of the algorithms or modelscan ... leadto algorithmic bias
data partiality(passive) caused byalgorithmic bias
the more data that gets fed to AI(passive) is createdAlgorithmic bias
Current AI tools ... inscrutable datacreatesalgorithmic bias
that programmers can pass their own unconscious prejudices on to the computers they ’re working onresultingin algorithmic bias
likely’ll createalgorithmic bias
it has to be implemented properly and with auditabilitycan leadto algorithmic bias
also(passive) was ... discoveredAlgorithmic bias
the sociotechnical processesleadingto algorithmic bias
Use Broad Data Samples — As seen in the Amazon example , using only historical data or a singular data sourcecan leadto algorithmic bias
polarization , misinformation , surveillance and inequityresultingfrom algorithmic bias
policiespreventalgorithmic bias
though we should be cautious since optimizing for single metricstypically leadsto algorithmic bias
The inadvertent negligenceleadsto algorithmic bias
its effortsto preventalgorithmic bias
an effortto preventalgorithmic bias
an all white , male team(passive) created bythe algorithmic bias
in the name of data minimizationcan leadto algorithmic bias
Pinboard bookmarks tagged hcm hcm 322 AI in hiringcan leadto algorithmic bias
awareness of data biascould resultin algorithmic bias
those decisionscan ... leadto algorithmic bias
injusticesmay resultfrom algorithmic bias
the appropriate stepsto preventalgorithmic bias
the potential health disparities in patient diagnosis and careresultfrom algorithmic bias
the high proportion of liberals employed in techleadsto algorithmic bias
putting the safeguards in placeto preventalgorithmic bias
any economic or other harmresultingfrom algorithmic bias
the four techniques currently being usedto preventalgorithmic bias
to social exclusion and discriminatory practicescan leadto social exclusion and discriminatory practices
to over - policing in predominately black areascan leadto over - policing in predominately black areas
to exclusionary experiences and discriminatory practices — especially against women and women of colorleadingto exclusionary experiences and discriminatory practices — especially against women and women of color
in underfunding for projects , racial discrimination , or other serious issuescan resultin underfunding for projects , racial discrimination , or other serious issues
to discriminatory practices and behaviors in societycan ... leadto discriminatory practices and behaviors in society
to greater unfairness ... in who gets what from the public pursecould leadto greater unfairness ... in who gets what from the public purse
to discrimination and unfair treatmentcan leadto discrimination and unfair treatment
in them policing certain areas more heavilymay resultin them policing certain areas more heavily
in both harms of allocation and harms of representationcan resultin both harms of allocation and harms of representation
or indirectly allow machines to learn prejudiced behaviorcan createor indirectly allow machines to learn prejudiced behavior
to discrimination against demographics who are not well represented in the training datacan leadto discrimination against demographics who are not well represented in the training data
to lawsuits under state or federal anti - discrimination statutesleadingto lawsuits under state or federal anti - discrimination statutes
to exclusionary and even discriminatory practices Design Thinking can assist and enhance the curation of the data that is required for AI feature engineering Design Thinkingleadsto exclusionary and even discriminatory practices Design Thinking can assist and enhance the curation of the data that is required for AI feature engineering Design Thinking
an increase in racism and gender discrimination on the internet ... alongside a sharp spike in cyberattackshas ... causedan increase in racism and gender discrimination on the internet ... alongside a sharp spike in cyberattacks
an information bubble and causes us for instance to pay too much for flight tickets or insurancescreatesan information bubble and causes us for instance to pay too much for flight tickets or insurances
to “ discrimination on the grounds of protected characteristics ” and “ outcomes and processes which are systematically less fair to individuals within a particular groupcould leadto “ discrimination on the grounds of protected characteristics ” and “ outcomes and processes which are systematically less fair to individuals within a particular group
problems in many cases ... Amazon ’s internal hiring tool that penalised female candidates , and facial recognition software found to be accurate only for fair - skinned men Techniques such as SHAP , which explain the predictions produced by machine learning models , can greatly reduce the risks associated with algorithmic bias and increase fairness and transparency in decisions taken by AI toolshas causedproblems in many cases ... Amazon ’s internal hiring tool that penalised female candidates , and facial recognition software found to be accurate only for fair - skinned men Techniques such as SHAP , which explain the predictions produced by machine learning models , can greatly reduce the risks associated with algorithmic bias and increase fairness and transparency in decisions taken by AI tools
AI systems to behave in unintended wayscan causeAI systems to behave in unintended ways
computers to produce homophobic , racist , and sexist resultscan ... leadcomputers to produce homophobic , racist , and sexist results
to machine learning algorithms misclassifying minority groupshas ledto machine learning algorithms misclassifying minority groups
from the data pool that was initially used to “ train ” an algorithm , from the perpetuation and accentuation of conscious or subconscious bias on the part of human trainers , or by coincidencecan resultfrom the data pool that was initially used to “ train ” an algorithm , from the perpetuation and accentuation of conscious or subconscious bias on the part of human trainers , or by coincidence
to negative consequences in the kinds of recommendations that are madecan leadto negative consequences in the kinds of recommendations that are made
to an increased efficiency of flawed decisionscan leadto an increased efficiency of flawed decisions
from choice data led to unpredictable biased results Moralresultingfrom choice data led to unpredictable biased results Moral
to the risk of stereotypingmay contributeto the risk of stereotyping
the bad outcomes(passive) caused bythe bad outcomes
to many false positivesto leadto many false positives
opinion fragmentation and enhances polarizationcreatesopinion fragmentation and enhances polarization
sloppy false positive and false negative rates because the program may try to balance the dataset by adding more data when there is not enough data available to the AI systemcreatessloppy false positive and false negative rates because the program may try to balance the dataset by adding more data when there is not enough data available to the AI system
unfair outcomesto createunfair outcomes
from the application of e.g. machine learning to data that is reflective of human biasresultingfrom the application of e.g. machine learning to data that is reflective of human bias
offensive tagging of online photos , and predatoryadvertisinghas resultedoffensive tagging of online photos , and predatoryadvertising
errors that may lead to unfair or dangerous outcomes , for instance , for one or more groups of people , organisations , living things and the environmentcreateserrors that may lead to unfair or dangerous outcomes , for instance , for one or more groups of people , organisations , living things and the environment
during an impact assessmentdiscoveredduring an impact assessment
in?these human tendenciesoriginatesin?these human tendencies
users to place too much confidence in the results achieved by the technology ... regardless of its real - world accuracy or effectivenessmay leadusers to place too much confidence in the results achieved by the technology ... regardless of its real - world accuracy or effectiveness
opinion splitting and fragmentation in the bounded confidence model ... by increasing the number of clusters with increasing biascausesopinion splitting and fragmentation in the bounded confidence model ... by increasing the number of clusters with increasing bias
to poor detection of faces that are not well represented in current training setsleadsto poor detection of faces that are not well represented in current training sets
harmto causeharm
from data infected by human prejudices ... and threats to privacy and securityresultsfrom data infected by human prejudices ... and threats to privacy and security