🔖 Big Data, Data Science, and Civil Rights
Solon Barocas, Elizabeth Bradley, Vasant Honavar, Foster Provost
🔗 https://arxiv.org/pdf/1706.03102
📌 ABSTRACT
Advances in data analytics bring with them civil rights implications. Data-driven and algorithmic decision making increasingly determine how businesses target advertisements to consumers, how police departments monitor individuals or groups, how banks decide who gets a loan and who does not, how employers hire, how colleges and universities make admissions and financial aid decisions, and much more. As data-driven decisions increasingly affect every corner of our lives, there is an urgent need to ensure they do not become instruments of discrimination, barriers to equality, threats to social justice, and sources of unfairness. In this paper, we argue for a concrete research agenda aimed at addressing these concerns, comprising five areas of emphasis: (i) Determining if models and modeling procedures exhibit objectionable bias; (ii) Building awareness of fairness into machine learning methods; (iii) Improving the transparency and control of data- and model-driven decision making; (iv) Looking beyond the algorithm(s) for sources of bias and unfairness-in the myriad human decisions made during the problem formulation and modeling process; and (v) Supporting the cross-disciplinary scholarship necessary to do all of that well.
Solon Barocas, Elizabeth Bradley, Vasant Honavar, Foster Provost
🔗 https://arxiv.org/pdf/1706.03102
📌 ABSTRACT
Advances in data analytics bring with them civil rights implications. Data-driven and algorithmic decision making increasingly determine how businesses target advertisements to consumers, how police departments monitor individuals or groups, how banks decide who gets a loan and who does not, how employers hire, how colleges and universities make admissions and financial aid decisions, and much more. As data-driven decisions increasingly affect every corner of our lives, there is an urgent need to ensure they do not become instruments of discrimination, barriers to equality, threats to social justice, and sources of unfairness. In this paper, we argue for a concrete research agenda aimed at addressing these concerns, comprising five areas of emphasis: (i) Determining if models and modeling procedures exhibit objectionable bias; (ii) Building awareness of fairness into machine learning methods; (iii) Improving the transparency and control of data- and model-driven decision making; (iv) Looking beyond the algorithm(s) for sources of bias and unfairness-in the myriad human decisions made during the problem formulation and modeling process; and (v) Supporting the cross-disciplinary scholarship necessary to do all of that well.
🔖 The physicist's guide to one of biotechnology's hottest new topics: CRISPR-Cas
Melia E. Bonomo, Michael W. Deem
🔗 arxiv.org/pdf/1712.09865.pdf
📌 ABSTRACT
Clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated proteins (Cas) constitute a multi-functional, constantly evolving immune system in bacteria and archaea cells. A heritable, molecular memory is generated of phage, plasmids, or other mobile genetic elements that attempt to attack the cell. This memory is used to recognize and interfere with subsequent invasions from the same genetic elements. This versatile prokaryotic tool has also been used to advance applications in biotechnology. Here we review a large body of CRISPR-Cas research to explore themes of evolution and selection, population dynamics, horizontal gene transfer, specific and cross-reactive interactions, cost and regulation, as well as non-defensive CRISPR functions that boost host cell robustness. Physical understanding of the CRISPR-Cas system will advance applications, such as efficient and specific genetic engineering, cell labeling and information storage, and combating antibiotic resistance.
Melia E. Bonomo, Michael W. Deem
🔗 arxiv.org/pdf/1712.09865.pdf
📌 ABSTRACT
Clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated proteins (Cas) constitute a multi-functional, constantly evolving immune system in bacteria and archaea cells. A heritable, molecular memory is generated of phage, plasmids, or other mobile genetic elements that attempt to attack the cell. This memory is used to recognize and interfere with subsequent invasions from the same genetic elements. This versatile prokaryotic tool has also been used to advance applications in biotechnology. Here we review a large body of CRISPR-Cas research to explore themes of evolution and selection, population dynamics, horizontal gene transfer, specific and cross-reactive interactions, cost and regulation, as well as non-defensive CRISPR functions that boost host cell robustness. Physical understanding of the CRISPR-Cas system will advance applications, such as efficient and specific genetic engineering, cell labeling and information storage, and combating antibiotic resistance.
💰 The relevance of thermodynamics to economics
https://en.wikipedia.org/wiki/Nicholas_Georgescu-Roegen#The_relevance_of_thermodynamics_to_economics
The physical theory of #thermodynamics is based on two laws: The first law states that energy is neither created nor destroyed in any isolated system (a conservation principle). The second law of thermodynamics — also known as the #entropy law — states that energy tends to be degraded to ever poorer qualities (a degradation principle).
Georgescu argues that the relevance of thermodynamics to #economics stems from the physical fact that man can neither create nor destroy matter or energy, only transform it. The usual economic terms of "#production" and "#consumption" are mere verbal conventions that tend to obscure that nothing is created and nothing is destroyed in the economic process — everything is being transformed.
The science of thermodynamics features a #cosmology of its own predicting the #heat_death_of_the_universe: Any transformation of energy — whether in nature or in human society — is moving the universe closer towards a final state of inert physical uniformity and #maximum_entropy. According to this cosmological perspective, all of man's economic activities are only speeding up the general march against a future planetary heat death locally on earth, Georgescu submits. This view on the economy was later termed '#entropy_pessimism'. Some of Georgescu's followers and interpreters have elaborated on this view.
https://en.wikipedia.org/wiki/Nicholas_Georgescu-Roegen#The_relevance_of_thermodynamics_to_economics
The physical theory of #thermodynamics is based on two laws: The first law states that energy is neither created nor destroyed in any isolated system (a conservation principle). The second law of thermodynamics — also known as the #entropy law — states that energy tends to be degraded to ever poorer qualities (a degradation principle).
Georgescu argues that the relevance of thermodynamics to #economics stems from the physical fact that man can neither create nor destroy matter or energy, only transform it. The usual economic terms of "#production" and "#consumption" are mere verbal conventions that tend to obscure that nothing is created and nothing is destroyed in the economic process — everything is being transformed.
The science of thermodynamics features a #cosmology of its own predicting the #heat_death_of_the_universe: Any transformation of energy — whether in nature or in human society — is moving the universe closer towards a final state of inert physical uniformity and #maximum_entropy. According to this cosmological perspective, all of man's economic activities are only speeding up the general march against a future planetary heat death locally on earth, Georgescu submits. This view on the economy was later termed '#entropy_pessimism'. Some of Georgescu's followers and interpreters have elaborated on this view.
Wikipedia
Nicholas Georgescu-Roegen
Mathematician, Statistician and Economist (1906-1994)
🥁 Networks Course blog - lots of interesting current events viewed from a network perspective:
http://blogs.cornell.edu/info2040/
http://blogs.cornell.edu/info2040/
Complex Systems Studies
networks-book.pdf
👉🏻 Section 16.3 in Easley and Kleinberg’s “Networks, Crowds, and Markets” is a nice and clear resource explaining #Bayes_Theorem in simple terms.
🎬 Full graduate course in #Bayesian statistics (videos + slides + homework).
https://www.zabaras.com/statisticalcomputing
#DataScience #MachineLearning
https://www.zabaras.com/statisticalcomputing
#DataScience #MachineLearning
home
Statisical Computing | University of Notre Dame
statisticalcomputing
Can you have access to Telegram even if it is blocked in your region?
Yes, but with some difficulites. – 32
👍👍👍👍👍👍👍 65%
Yes. – 14
👍👍👍 29%
No. – 3
👍 6%
👥 49 people voted so far. Poll closed.
Yes, but with some difficulites. – 32
👍👍👍👍👍👍👍 65%
Yes. – 14
👍👍👍 29%
No. – 3
👍 6%
👥 49 people voted so far. Poll closed.
Complex Systems Studies pinned «Can you have access to Telegram even if it is blocked in your region? Yes, but with some difficulites. – 32 👍👍👍👍👍👍👍 65% Yes. – 14 👍👍👍 29% No. – 3 👍 6% 👥 49 people voted so far. Poll closed.»
Life’s complex genetic code could have evolved from a primitive biochemical system based on a feedback loop and just two rules.
https://t.co/9bt35zTphK
https://t.co/9bt35zTphK
⌨ Terrific essay by Rodney Brooks on #machine_learning, with lots of the early history, including matchbox tic-tac-toe!
http://rodneybrooks.com/forai-machine-learning-explained/
http://rodneybrooks.com/forai-machine-learning-explained/