Data-driven reporting - why do it.pptx
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An introduction to how data journalism can contribute / add to or spearhead news reporting. A few highly visible examples and how they were achieved.
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algorithmic-bias.pptx
1) Automated decision-making systems are opaque and can encode hidden biases, but human decisions also exhibit bias. It is mathematically impossible for algorithms to achieve both equal predictive value and equal false positive rates across groups with different base rates.
2) Studies show discrimination in online marketplaces, where requests from users with black-sounding names receive fewer responses from hosts on AirBnB and ads featuring arrest records appear more for searches with black-sounding names on Google.
3) Gerrymandering of electoral districts to benefit one party can violate equal treatment of voters, but defining and preventing partisan bias through redistricting is challenging, as natural
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algorithmic-bias.pptx
1) Automated decision-making systems are opaque and can encode hidden biases, but human decisions also exhibit bias. It is mathematically impossible for algorithms to achieve both equal predictive value and equal false positive rates across groups with different base rates.
2) Studies show discrimination in online marketplaces, where requests from users with black-sounding names receive fewer responses from hosts on AirBnB and ads featuring arrest records appear more for searches with black-sounding names on Google.
3) Gerrymandering of electoral districts to benefit one party can violate equal treatment of voters, but defining and preventing partisan bias through redistricting is challenging, as natural
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This document summarizes a webinar on data ethics when designing civil justice interventions. It provides an agenda for the webinar which includes introductions, a discussion of how machines can learn to discriminate with Solon Barocas speaking, and a discussion of digital decision making with Ali Lange speaking. It also includes information about the speakers and a question period. Key topics discussed are how big data can unintentionally reinforce biases and disparities if human oversight is lacking, and the importance of considering data sensitivities, consumer protection laws, and empowering clients when using big data.
Data and Algorithmic Bias in the Web
The Web is the largest public big data repository that humankind has created. In this overwhelming data ocean, we need to be aware of the quality and, in particular, of the biases that exist in this data. In the Web, biases also come from redundancy and spam, as well as from algorithms that we design to improve the user experience. This problem is further exacerbated by biases that are added by these algorithms, specially in the context of search and recommendation systems. They include selection and presentation bias in many forms, interaction bias, social bias, etc. We give several examples and their relation to sparsity and privacy, stressing the importance of the user context to avoid these biases.
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The U.S. Department of Commerce collects, processes and disseminates data on a range of issues that impact our nation. Whether it's data on the economy, the environment, or technology, data is critical in fulfilling the Department's mission of creating the conditions for economic growth and opportunity. It is this data that provides insight, drives innovation, and transforms our lives. The U.S. Department of Commerce has become known as "America's Data Agency" due to the tens of thousands of datasets including satellite imagery, material standards and demographic surveys.
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- Today's class on ethics and law in electronic reporting
- Future classes on ethics and law regarding radio, online/social media, and newsroom roles
- An introduction explaining this class provides the foundation for further electronic news production courses
It then discusses the difference between law and ethics, commonly accepted ethics practices, and resources from organizations like the RTDNA and SPJ regarding journalism ethics codes.
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http://www.wolframdatasummit.org/2011/attendee/presentations/#Adler
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- Today's class on ethics and law in electronic reporting
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- An introduction explaining this class provides the foundation for further electronic news production courses
It then discusses the difference between law and ethics, commonly accepted ethics practices, and resources from organizations like the RTDNA and SPJ regarding journalism ethics codes.
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http://www.wolframdatasummit.org/2011/attendee/presentations/#Adler
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"Big data" has been around for a few years now but for every hundred people talking about it there’s probably only one actually doing it. As a result Big Data has become the preferred vehicle for inflated expectations and misguided strategy.
As always, the seed of the issue is in the expression itself. Big Data is not so much about a quality of the data or the tools to mine it, it’s about a new approach to product, policy or business strategy design. And that’s way harder and trickier to implement than any new technology stack.
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The document discusses fairness, accountability, and transparency (FAT) in recommender systems. It begins with an introduction of the presenter, Denis Parra, who is an associate professor in Chile studying recommender systems. The presentation then discusses some examples of recent advances in artificial intelligence like natural language processing, self-driving cars, and mastering the game of Go. However, it also notes there are some problems with bias in AI systems that affect areas like criminal risk assessments and facial analysis. The presentation suggests recommender systems can also be affected by these issues and discusses ways researchers are working to address fairness, explainability, and transparency in machine learning models and applications like recommender systems.
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This document discusses challenges with big data and data analytics, including data bias, data manipulation, and lack of transparency and accountability. It provides examples of each challenge. For data bias, it discusses Google Flu Trends being inaccurate due to unrelated seasonal terms in the input data. For facial recognition technology, it discusses biases in the limited training data used. For data manipulation, it discusses how visualizations can exaggerate or omit unwanted data. It also provides examples of risk assessment algorithms and credit scoring lacking transparency. The document concludes by suggesting ways to address these issues, such as ensuring diverse test data and explaining data use and limitations.
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Fairness-aware Machine Learning: Practical Challenges and Lessons Learned (KD...
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Find Latest India News and Breaking News these days from India on Politics, Business, Entertainment, Technology, Sports, Lifestyle and Coronavirus News in India and the world over that you can't miss. For real time update Visit our social media handle. Read First India NewsPaper in your morning replace. Visit First India.
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如果您处于以下几种情况：
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留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
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Howard Fineman, Veteran Political Journalist and TV Pundit, Dies at 75
Howard Fineman, Veteran Political Journalist and TV Pundit, Dies at 75
Data-driven reporting - why do it.pptx
- 1. Data-driven reporting: why do it? CRINA–GABRIELA BOROŞ DATA JOURNALIST & INTERNATIONAL CAR TRAINER
- 3. What’s in a name? Computer-Assisted Reporting (CAR) Data Journalism (DJ) Data-Driven Journalism (DDJ) There is no good name for what we do! DATA = JUST ANOTHER SOURCE What CAR reporters are not = hackers / programmers, web designers, scientists, mathematicians
- 4. TRANSPARENCY ACCOUNTABILITY EVIDENCE-BASED JOURNALISM (as opposed to anecdotal evidence or informed opinion) SHOWS SYSTEMIC PROBLEMS IDENTIFIES OUTLIERS OUTSIDE PERSONAL BIAS OR ANECDOTES PROTECTION OF HUMAN SOURCES FACILITATES WATCHDOGGING
- 5. So, why does it matter? Proof of systemic wrongdoing *stop and search discriminates against non-whites Correlations *school test results and poverty level Can find the problems in the system *cartels *fat cats *discrimination Misuse of public trust * MPs expenses * Trends over time *life expectancy
- 8. OUR PROCESS
- 9. : FQA Register Data engineering Follow-the-money – corporate registries FOI requests Clever web searches Merging several datasets Analysis Auditing Reporting and research throughout Confrontation interviews Right to reply Writing and publication
- 10. Image by Greenpeace UK
- 12. Meet Nina May
- 13. LINKS: UK FQA (Fixed Quota Allocation) Register A list of fishing vessel licences and entitlement owners who hold FQA units https://www.fqaregister.service.gov.uk/ Annual Tonnage Allocations: tonnes of fish per FQA unit It varies year on year Company registers https://www.duedil.com/ https://www.gov.uk/government/organisations/companies-house https://opencorporates.com https://investigativedashboard.org/ Vessel tracking websites
- 14. EUobserver HOW THE EU COSIED UP TO THE DEFENCE LOBBY
- 15. Findings: Open procurement data People background checks Freedom of Information logs Advisory Committees and procurement winners are in conflict of interest, often undeclared UK expert refused to file her DOI An unnecessary and expensive border surveillance system No one is counting the dead Interest groups supply public funds bloodlines to defence companies rebranded as ‘security services’
- 16. LINKS: Ask the EU https://www.asktheeu.org/ EU Tenders databases http://data.europa.eu/euodp/data/dataset/ted-1 https://ted.europa.eu/TED/main/HomePage.do Committees and special advisors lists https://ec.europa.eu/info/about-european- commission/service-standards-and- principles/transparency/special-advisers_en Lobby registers http://ec.europa.eu/transparencyregister/public/home Page.do
- 19. RESOURCES: Income data (public government agency) NGO-collected socio-economic data Union data & reports Migration figures Anecdotal evidence Public documents Corporate PR Field reporting & interviews Vulnerable source protection