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Boro Park Jcc Breakfast Highlights Continued Service To Community, Bias Is To Fairness As Discrimination Is To

Those looking for work can sign up for career training and employment services, while specialized support will be offered for seniors and Holocaust survivors. We Need to Tell Boro Park’s Story      –. Compare nonprofit financials to similar organizations. Manhattan Borough President Scott Stringer visited the offices of the Boro Park Jewish Community Council. Media matters, because the narrative that New Yorkers read about us determines how safe our streets are.

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Boro Park Jewish Community Council, in partnership with Met Council SNAP (Supplemental Nutrition Assistance Program) Specialists, are pleased to offer services to help the community gain efficient access to public benefits through individualized appointments and services. Buery was honored for his role as the architect of the Universal Pre K program as well as many other initiatives which have brought tremendous benefits to many in the Boro Park and other communities. If you believe in the governing principles of this website – to help effect positive change through the candid discussions of the real issues we collectively face, please consider becoming a daily, weekly or monthly sponsor of this website and help defray the costs of it's maintenance. Boro park jewish community council of coney island. A Jewish home, where all are welcome. Metropolitan Council of Boro Park Jewish Community Council.

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JCC Has moved to 1310 46th St 718 972-6600. Community Tribute Breakfast. It's high time for us to allocate the community resources needed to actively shape that media perception. "We are incredibly proud to team up with the Boro Park JCC to bring this lifesaving program to more of our neighbors. I commend BPJCC and the American Red Cross for working to protect the safety of Boro Park residents and mitigate the potentially catastrophic effects of fire and other emergency circumstances. Met Council is the umbrella organization for 16 local Jewish Community Councils throughout New York City. Boro Park JCC Breakfast Highlights Continued Service to Community. Brooklyn Hub — Coming Soon. Met Council's multilingual staff will help Boro Park residents with everything from Section 8 and rent freeze programs to emergency and Shabbos food packages. The Red Cross is a not-for-profit organization that depends on volunteers and the generosity of the American public to perform its mission.

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Analyze a variety of pre-calculated financial metrics. Jewish Community Council of Greater Coney Island. Initially the partnership will focus on fire safety. On average, 7 people die from a home fire every day in the US; on average, 36 people suffer injuries from home fires every day in the US. Via local tabling, canvassing events and outreach efforts online, the Red Cross and BPJCC will work closely from the offices of the BPJCC to register as many local families as possible to have free smoke alarms installed in their homes by volunteers as well as to provide fire safety education to the community. Jewish Community Council Network. This new initiative was created because it is an idea whose time has come. The Red Cross and the BPJCC were on the scene to offer emergency assistance.

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Williamsburg Yeshivas Education Services Project. Our focus is helping each individual that walks through our door, no matter who they are or where they are coming from. Executive Director: Avi Greenstein. Phone: 718-377-2900 Fax: (718) 377-6089. PHOTO CAPTION: L-R- Rabbi Yitzchok Fleischer, Board Member BPJCC, Rabbi Yeruchim Silber, Executive Director, BPJCC, Commissioner Steve Banks, Councilman Brad Lander, MC Menachem Lubinsky, BPJCC President Isaac Stern. For years, Kiryas Joel was depicted in the media as the poorest district in the United States. Boro park jewish community council of pelham parkway. Then, a simple study by our friends at the Orthodox Jewish Public Affairs Council exposed how this data had been cherry-picked to fit a narrative. Phone: 718-643-9700 Fax: 718-643-3365. Moving forward, the partnership will expand to include more preparedness initiatives and volunteer recruitment efforts. We can do better than that. We provide social services and referrals to assist members of the community to get what they need.

"A working smoke detector and proper fire safety awareness can literally mean the difference between life and death. This service is available during the winter months and can subsidize home heating bills. In New York City, the FDNY's Bureau of Fire Investigations has determined that up to 70 percent of fire deaths in recent years have occurred in residences where there was no working smoke alarm – either with no alarm present, or missing or dead batteries in a device. Your financial support can allow us to expand these services and help more children. Social services and direct food assistance will be provided by Met Council to support the community and help families struggling in the wake of the pandemic and the economic downturn. 1170 Pennsylvania Avenue, Suite 1B, Brooklyn, NY 11239. Brooklyn families love giving back — and even better, we want to schmooze and connect while we do it! Phone: 718 972 6600. The underlying theme of the breakfast was highlighting community service and indeed all the honorees were feted for their remarkable contributions to the community at large, each in their own field. And in a most poignant address Espinal, honored for his leading role in spearheading the City Council Holocaust Survivor Initiative, recalled his visit to Auschwitz which moved him to do something meaningful for the thousands of holocaust survivors living in poverty, even though none live in his own district. Greater Williamsburg Collaborative (St. Boro park jewish community council of rockaway peninsula. Nicholas NPC, Los Sures, Brooklyn Navy Yard Development Corporation and United Jewish Organizations of Williamsburg).

Borgesius, F. : Discrimination, Artificial Intelligence, and Algorithmic Decision-Making. As data practitioners we're in a fortunate position to break the bias by bringing AI fairness issues to light and working towards solving them. For instance, given the fundamental importance of guaranteeing the safety of all passengers, it may be justified to impose an age limit on airline pilots—though this generalization would be unjustified if it were applied to most other jobs. The idea that indirect discrimination is only wrongful because it replicates the harms of direct discrimination is explicitly criticized by some in the contemporary literature [20, 21, 35]. As mentioned above, we can think of putting an age limit for commercial airline pilots to ensure the safety of passengers [54] or requiring an undergraduate degree to pursue graduate studies – since this is, presumably, a good (though imperfect) generalization to accept students who have acquired the specific knowledge and skill set necessary to pursue graduate studies [5]. Prejudice, affirmation, litigation equity or reverse. Bias is to fairness as discrimination is to influence. The issue of algorithmic bias is closely related to the interpretability of algorithmic predictions. We identify and propose three main guidelines to properly constrain the deployment of machine learning algorithms in society: algorithms should be vetted to ensure that they do not unduly affect historically marginalized groups; they should not systematically override or replace human decision-making processes; and the decision reached using an algorithm should always be explainable and justifiable.

Bias Is To Fairness As Discrimination Is To Free

For instance, if we are all put into algorithmic categories, we could contend that it goes against our individuality, but that it does not amount to discrimination. Footnote 16 Eidelson's own theory seems to struggle with this idea. Troublingly, this possibility arises from internal features of such algorithms; algorithms can be discriminatory even if we put aside the (very real) possibility that some may use algorithms to camouflage their discriminatory intents [7]. This may amount to an instance of indirect discrimination. News Items for February, 2020. Two aspects are worth emphasizing here: optimization and standardization. Yet, it would be a different issue if Spotify used its users' data to choose who should be considered for a job interview. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Bias is to fairness as discrimination is to free. 2016): calibration within group and balance. 2009 2nd International Conference on Computer, Control and Communication, IC4 2009. In other words, a probability score should mean what it literally means (in a frequentist sense) regardless of group.

Alexander, L. : What makes wrongful discrimination wrong? There is evidence suggesting trade-offs between fairness and predictive performance. Insurance: Discrimination, Biases & Fairness. Thirdly, and finally, it is possible to imagine algorithms designed to promote equity, diversity and inclusion. After all, as argued above, anti-discrimination law protects individuals from wrongful differential treatment and disparate impact [1]. It is important to keep this in mind when considering whether to include an assessment in your hiring process—the absence of bias does not guarantee fairness, and there is a great deal of responsibility on the test administrator, not just the test developer, to ensure that a test is being delivered fairly.

Bias Is To Fairness As Discrimination Is To Influence

It's also worth noting that AI, like most technology, is often reflective of its creators. Barocas, S., & Selbst, A. Relationship between Fairness and Predictive Performance. We come back to the question of how to balance socially valuable goals and individual rights in Sect. For instance, the four-fifths rule (Romei et al. Therefore, the use of ML algorithms may be useful to gain in efficiency and accuracy in particular decision-making processes. Taylor & Francis Group, New York, NY (2018). Maclure, J. and Taylor, C. AI’s fairness problem: understanding wrongful discrimination in the context of automated decision-making. : Secularism and Freedom of Consicence. Rawls, J. : A Theory of Justice.

First, there is the problem of being put in a category which guides decision-making in such a way that disregards how every person is unique because one assumes that this category exhausts what we ought to know about us. Introduction to Fairness, Bias, and Adverse Impact. For example, when base rate (i. e., the actual proportion of. How should the sector's business model evolve if individualisation is extended at the expense of mutualisation? 2] Moritz Hardt, Eric Price,, and Nati Srebro.

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O'Neil, C. : Weapons of math destruction: how big data increases inequality and threatens democracy. 3, the use of ML algorithms raises the question of whether it can lead to other types of discrimination which do not necessarily disadvantage historically marginalized groups or even socially salient groups. In contrast, disparate impact discrimination, or indirect discrimination, captures cases where a facially neutral rule disproportionally disadvantages a certain group [1, 39]. On Fairness and Calibration. Calders and Verwer (2010) propose to modify naive Bayes model in three different ways: (i) change the conditional probability of a class given the protected attribute; (ii) train two separate naive Bayes classifiers, one for each group, using data only in each group; and (iii) try to estimate a "latent class" free from discrimination. Unlike disparate impact, which is intentional, adverse impact is unintentional in nature. This points to two considerations about wrongful generalizations. Bias is to fairness as discrimination is to review. By (fully or partly) outsourcing a decision to an algorithm, the process could become more neutral and objective by removing human biases [8, 13, 37]. Building classifiers with independency constraints.

Rather, these points lead to the conclusion that their use should be carefully and strictly regulated. Top 6 Effective Tips On Creating Engaging Infographics - February 24, 2023. Data preprocessing techniques for classification without discrimination. 2018) discuss this issue, using ideas from hyper-parameter tuning. Such labels could clearly highlight an algorithm's purpose and limitations along with its accuracy and error rates to ensure that it is used properly and at an acceptable cost [64]. A statistical framework for fair predictive algorithms, 1–6. Putting aside the possibility that some may use algorithms to hide their discriminatory intent—which would be an instance of direct discrimination—the main normative issue raised by these cases is that a facially neutral tool maintains or aggravates existing inequalities between socially salient groups. The Washington Post (2016). Point out, it is at least theoretically possible to design algorithms to foster inclusion and fairness. In particular, it covers two broad topics: (1) the definition of fairness, and (2) the detection and prevention/mitigation of algorithmic bias. Retrieved from - Chouldechova, A. Fully recognize that we should not assume that ML algorithms are objective since they can be biased by different factors—discussed in more details below.

Pos in a population) differs in the two groups, statistical parity may not be feasible (Kleinberg et al., 2016; Pleiss et al., 2017). Chun, W. : Discriminating data: correlation, neighborhoods, and the new politics of recognition. Public and private organizations which make ethically-laden decisions should effectively recognize that all have a capacity for self-authorship and moral agency. A philosophical inquiry into the nature of discrimination. Automated Decision-making. The preference has a disproportionate adverse effect on African-American applicants. For instance, it is doubtful that algorithms could presently be used to promote inclusion and diversity in this way because the use of sensitive information is strictly regulated. A Convex Framework for Fair Regression, 1–5. Next, it's important that there is minimal bias present in the selection procedure. Footnote 2 Despite that the discriminatory aspects and general unfairness of ML algorithms is now widely recognized in academic literature – as will be discussed throughout – some researchers also take the idea that machines may well turn out to be less biased and problematic than humans seriously [33, 37, 38, 58, 59]. Importantly, such trade-off does not mean that one needs to build inferior predictive models in order to achieve fairness goals. Though these problems are not all insurmountable, we argue that it is necessary to clearly define the conditions under which a machine learning decision tool can be used.

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