#algorithmicfairness — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #algorithmicfairness, aggregated by home.social.
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Algorithmic redistricting sounds like a clean fix for gerrymandering, but it’s just as vulnerable to bias as human mapmakers. The algorithm will only be fair if we hard-code transparency and clear ethical constraints from the start - otherwise it just automates the manipulation. #redistricting #algorithmicfairness #votingrights
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Algorithmic redistricting sounds like a clean fix for gerrymandering, but it’s just as vulnerable to bias as human mapmakers. The algorithm will only be fair if we hard-code transparency and clear ethical constraints from the start - otherwise it just automates the manipulation. #redistricting #algorithmicfairness #votingrights
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Algorithmic redistricting sounds like a clean fix for gerrymandering, but it’s just as vulnerable to bias as human mapmakers. The algorithm will only be fair if we hard-code transparency and clear ethical constraints from the start - otherwise it just automates the manipulation. #redistricting #algorithmicfairness #votingrights
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Algorithmic redistricting sounds like a clean fix for gerrymandering, but it’s just as vulnerable to bias as human mapmakers. The algorithm will only be fair if we hard-code transparency and clear ethical constraints from the start - otherwise it just automates the manipulation. #redistricting #algorithmicfairness #votingrights
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TODAY: Join CDT’s Miranda Bogen for a PAI Partner Roundtable on Algorithmic Fairness & Demographic Data where she will be joining Eliza McCullough, Janet Haven, and Daniel Ho. Tune in LIVE at 12 ET. #AlgorithmicFairness #AI https://cdt.org/event/pai-partner-roundtable-demographic-data-algorithmic-fairness/
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TODAY: Join CDT’s Miranda Bogen for a PAI Partner Roundtable on Algorithmic Fairness & Demographic Data where she will be joining Eliza McCullough, Janet Haven, and Daniel Ho. Tune in LIVE at 12 ET. #AlgorithmicFairness #AI https://cdt.org/event/pai-partner-roundtable-demographic-data-algorithmic-fairness/
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TODAY: Join CDT’s Miranda Bogen for a PAI Partner Roundtable on Algorithmic Fairness & Demographic Data where she will be joining Eliza McCullough, Janet Haven, and Daniel Ho. Tune in LIVE at 12 ET. #AlgorithmicFairness #AI https://cdt.org/event/pai-partner-roundtable-demographic-data-algorithmic-fairness/
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TODAY: Join CDT’s Miranda Bogen for a PAI Partner Roundtable on Algorithmic Fairness & Demographic Data where she will be joining Eliza McCullough, Janet Haven, and Daniel Ho. Tune in LIVE at 12 ET. #AlgorithmicFairness #AI https://cdt.org/event/pai-partner-roundtable-demographic-data-algorithmic-fairness/
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TODAY: Join CDT’s Miranda Bogen for a PAI Partner Roundtable on Algorithmic Fairness & Demographic Data where she will be joining Eliza McCullough, Janet Haven, and Daniel Ho. Tune in LIVE at 12 ET. #AlgorithmicFairness #AI https://cdt.org/event/pai-partner-roundtable-demographic-data-algorithmic-fairness/
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Importantly, standard #algorithmicfairness solutions are strictly limited in what they can achieve in this regard: if the statistical relationship between inputs and outputs is simply more noisy in some group, no amount of "fair learning" can fix this!
In the paper (co-authored with Sune Holm, @melanieganzben1, Aasa Feragen), we discuss many more concrete medical examples of the different sources of bias, and we propose some tentative solution approaches. 6/N
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Importantly, standard #algorithmicfairness solutions are strictly limited in what they can achieve in this regard: if the statistical relationship between inputs and outputs is simply more noisy in some group, no amount of "fair learning" can fix this!
In the paper (co-authored with Sune Holm, @melanieganzben1, Aasa Feragen), we discuss many more concrete medical examples of the different sources of bias, and we propose some tentative solution approaches. 6/N
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Importantly, standard #algorithmicfairness solutions are strictly limited in what they can achieve in this regard: if the statistical relationship between inputs and outputs is simply more noisy in some group, no amount of "fair learning" can fix this!
In the paper (co-authored with Sune Holm, @melanieganzben1, Aasa Feragen), we discuss many more concrete medical examples of the different sources of bias, and we propose some tentative solution approaches. 6/N
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Importantly, standard #algorithmicfairness solutions are strictly limited in what they can achieve in this regard: if the statistical relationship between inputs and outputs is simply more noisy in some group, no amount of "fair learning" can fix this!
In the paper (co-authored with Sune Holm, @melanieganzben1, Aasa Feragen), we discuss many more concrete medical examples of the different sources of bias, and we propose some tentative solution approaches. 6/N
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Importantly, standard #algorithmicfairness solutions are strictly limited in what they can achieve in this regard: if the statistical relationship between inputs and outputs is simply more noisy in some group, no amount of "fair learning" can fix this!
In the paper (co-authored with Sune Holm, @melanieganzben1, Aasa Feragen), we discuss many more concrete medical examples of the different sources of bias, and we propose some tentative solution approaches. 6/N
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How to fix this? The consequentialist framework (CF) to algorithmic fairness foregrounds the results of decisions, rather than properties of the prediction.
One starts by identifying the utility of different possible outcomes, eg efficiency and equity. Optimal decision policies can be derived with Linear Programming that uses stakeholder preferences.
This approach has advantages over static experimental designs (eg randomized trials)
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How to fix this? The consequentialist framework (CF) to algorithmic fairness foregrounds the results of decisions, rather than properties of the prediction.
One starts by identifying the utility of different possible outcomes, eg efficiency and equity. Optimal decision policies can be derived with Linear Programming that uses stakeholder preferences.
This approach has advantages over static experimental designs (eg randomized trials)
-
How to fix this? The consequentialist framework (CF) to algorithmic fairness foregrounds the results of decisions, rather than properties of the prediction.
One starts by identifying the utility of different possible outcomes, eg efficiency and equity. Optimal decision policies can be derived with Linear Programming that uses stakeholder preferences.
This approach has advantages over static experimental designs (eg randomized trials)
-
How to fix this? The consequentialist framework (CF) to algorithmic fairness foregrounds the results of decisions, rather than properties of the prediction.
One starts by identifying the utility of different possible outcomes, eg efficiency and equity. Optimal decision policies can be derived with Linear Programming that uses stakeholder preferences.
This approach has advantages over static experimental designs (eg randomized trials)
-
How to fix this? The consequentialist framework (CF) to algorithmic fairness foregrounds the results of decisions, rather than properties of the prediction.
One starts by identifying the utility of different possible outcomes, eg efficiency and equity. Optimal decision policies can be derived with Linear Programming that uses stakeholder preferences.
This approach has advantages over static experimental designs (eg randomized trials)
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The latest turn in the #algorithmicfairness debate is "leveling up":
https://www.wired.com/story/bias-statistics-artificial-intelligence-healthcare/
Striking:
"Technical solutions are often only a Band-aid to deal with a broken system. Improving access to health care, curating more diverse data sets, and developing tools that specifically target the problems faced by historically disadvantaged communities can help make substantive fairness a reality."
Not: no technical solution at all but only within - may I say - a scoiotechnical system.
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The latest turn in the #algorithmicfairness debate is "leveling up":
https://www.wired.com/story/bias-statistics-artificial-intelligence-healthcare/
Striking:
"Technical solutions are often only a Band-aid to deal with a broken system. Improving access to health care, curating more diverse data sets, and developing tools that specifically target the problems faced by historically disadvantaged communities can help make substantive fairness a reality."
Not: no technical solution at all but only within - may I say - a scoiotechnical system.
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Excerpts from the article:
The majority of algorithms developed to enforce “algorithmic fairness” were built without #policy and societal contexts in mind.Our motivation for pursuing fairness is to improve the situation of a historically disadvantaged group.
When we build AI systems to make decisions about people's lives, our design decisions encode implicit value judgments about what should be prioritized.
Technical solutions are often only a Band-aid to deal with a broken system. Improving access to #HealthCare, curating more diverse data sets, and developing tools that specifically target the problems faced by historically disadvantaged communities can help make substantive fairness a reality.
#AI systems make life-changing decisions. Choices about how they should be fair, and to whom, are too important to treat #fairness as a simple mathematical problem to be solved.
#AlgorithmicFairness #MedicalSystem #AIEthics #FairML #ArtificialIntelligence
Article:
HealthCare #Bias Is Dangerous. But So Are ‘Fairness’ #AlgorithmsPaper:
The Unfairness of Fair #MachineLearning: Levelling down and strict egalitarianism by default -
Excerpts from the article:
The majority of algorithms developed to enforce “algorithmic fairness” were built without #policy and societal contexts in mind.Our motivation for pursuing fairness is to improve the situation of a historically disadvantaged group.
When we build AI systems to make decisions about people's lives, our design decisions encode implicit value judgments about what should be prioritized.
Technical solutions are often only a Band-aid to deal with a broken system. Improving access to #HealthCare, curating more diverse data sets, and developing tools that specifically target the problems faced by historically disadvantaged communities can help make substantive fairness a reality.
#AI systems make life-changing decisions. Choices about how they should be fair, and to whom, are too important to treat #fairness as a simple mathematical problem to be solved.
#AlgorithmicFairness #MedicalSystem #AIEthics #FairML #ArtificialIntelligence
Article:
HealthCare #Bias Is Dangerous. But So Are ‘Fairness’ #AlgorithmsPaper:
The Unfairness of Fair #MachineLearning: Levelling down and strict egalitarianism by default -
Excerpts from the article:
The majority of algorithms developed to enforce “algorithmic fairness” were built without #policy and societal contexts in mind.Our motivation for pursuing fairness is to improve the situation of a historically disadvantaged group.
When we build AI systems to make decisions about people's lives, our design decisions encode implicit value judgments about what should be prioritized.
Technical solutions are often only a Band-aid to deal with a broken system. Improving access to #HealthCare, curating more diverse data sets, and developing tools that specifically target the problems faced by historically disadvantaged communities can help make substantive fairness a reality.
#AI systems make life-changing decisions. Choices about how they should be fair, and to whom, are too important to treat #fairness as a simple mathematical problem to be solved.
#AlgorithmicFairness #MedicalSystem #AIEthics #FairML #ArtificialIntelligence
Article:
HealthCare #Bias Is Dangerous. But So Are ‘Fairness’ #AlgorithmsPaper:
The Unfairness of Fair #MachineLearning: Levelling down and strict egalitarianism by default -
Excerpts from the article:
The majority of algorithms developed to enforce “algorithmic fairness” were built without #policy and societal contexts in mind.Our motivation for pursuing fairness is to improve the situation of a historically disadvantaged group.
When we build AI systems to make decisions about people's lives, our design decisions encode implicit value judgments about what should be prioritized.
Technical solutions are often only a Band-aid to deal with a broken system. Improving access to #HealthCare, curating more diverse data sets, and developing tools that specifically target the problems faced by historically disadvantaged communities can help make substantive fairness a reality.
#AI systems make life-changing decisions. Choices about how they should be fair, and to whom, are too important to treat #fairness as a simple mathematical problem to be solved.
#AlgorithmicFairness #MedicalSystem #AIEthics #FairML #ArtificialIntelligence
Article:
HealthCare #Bias Is Dangerous. But So Are ‘Fairness’ #AlgorithmsPaper:
The Unfairness of Fair #MachineLearning: Levelling down and strict egalitarianism by default -
#AIEthics #MachineLearning #ArtificialIntelligence #AlgorithmicFairness #Operationalization
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"Operationalization"
It's not an easy word to say. Somehow I always end up putting an extra "z" in there. My friends find that quite amusing, though probably not as amusing as hearing me say "nuclear" in my native Midwestern.
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#AIEthics #MachineLearning #ArtificialIntelligence #AlgorithmicFairness #Operationalization
---
"Operationalization"
It's not an easy word to say. Somehow I always end up putting an extra "z" in there. My friends find that quite amusing, though probably not as amusing as hearing me say "nuclear" in my native Midwestern.
-
#AIEthics #MachineLearning #ArtificialIntelligence #AlgorithmicFairness #Operationalization
---
"Operationalization"
It's not an easy word to say. Somehow I always end up putting an extra "z" in there. My friends find that quite amusing, though probably not as amusing as hearing me say "nuclear" in my native Midwestern.
-
#AIEthics #MachineLearning #ArtificialIntelligence #AlgorithmicFairness #Operationalization
---
"Operationalization"
It's not an easy word to say. Somehow I always end up putting an extra "z" in there. My friends find that quite amusing, though probably not as amusing as hearing me say "nuclear" in my native Midwestern.
-
#AIEthics #MachineLearning #ArtificialIntelligence #AlgorithmicFairness #Operationalization
---
"Operationalization"
It's not an easy word to say. Somehow I always end up putting an extra "z" in there. My friends find that quite amusing, though probably not as amusing as hearing me say "nuclear" in my native Midwestern.
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@TimnitGebru Hi there, this is very encouraging news! I've been looking for a network that specifically talks about topics in #ethicalAI, #algorithmicFairness and alike on mastodon as no doubt have others.
One of the big issues with decentralised social networks like this (that don't use a block-chain) is trust. DMs are not private and so it's super important that the administrator is trusted because they have access to everything, nothing is private to them. #privacy.
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@TimnitGebru Hi there, this is very encouraging news! I've been looking for a network that specifically talks about topics in #ethicalAI, #algorithmicFairness and alike on mastodon as no doubt have others.
One of the big issues with decentralised social networks like this (that don't use a block-chain) is trust. DMs are not private and so it's super important that the administrator is trusted because they have access to everything, nothing is private to them. #privacy.
-
@TimnitGebru Hi there, this is very encouraging news! I've been looking for a network that specifically talks about topics in #ethicalAI, #algorithmicFairness and alike on mastodon as no doubt have others.
One of the big issues with decentralised social networks like this (that don't use a block-chain) is trust. DMs are not private and so it's super important that the administrator is trusted because they have access to everything, nothing is private to them. #privacy.
-
@TimnitGebru Hi there, this is very encouraging news! I've been looking for a network that specifically talks about topics in #ethicalAI, #algorithmicFairness and alike on mastodon as no doubt have others.
One of the big issues with decentralised social networks like this (that don't use a block-chain) is trust. DMs are not private and so it's super important that the administrator is trusted because they have access to everything, nothing is private to them. #privacy.
