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Tag: Fairness
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Vaseline Men SPF 30 PA+++ Total Fairness Serum UV Protection Moisturizer 50g
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#Vaseline #Men #SPF #Total #Fairness #Serum #Protection #Moisturizer #50g,ages 3+Preparing retirees for changes in Social Security Fairness Act
Preparing retirees for changes in Social Security Fairness Act
Updated: 4:35 PM PST Jan 22, 2025
WELL, THIS MORNING, THERE’S SOME GOOD NEWS FOR THE NEARLY 3 MILLION FORMER PUBLIC SERVICE EMPLOYEES WHO PAID INTO SOCIAL SECURITY BUT WERE SUBJECT TO A REDUCTION OR ELIMINATION OF BENEFITS BECAUSE OF THE PENSION OFFSETS. THE SOCIAL SECURITY FAIRNESS ACT OF 2024 JUST SIGNED A COUPLE OF WEEKS AGO. IT WILL AFFECT MILLIONS OF RETIREES, MOST NOTABLY FOLKS LIKE TEACHERS, FIREFIGHTERS AND POLICE OFFICERS. SO HERE TO GIVE US A BREAKDOWN OF THE DETAILS ON THIS NEW LAW, IS CAPTRUST VICE PRESIDENT AND FINANCIAL ADVISOR, CATHERINE MCCALL. THANK YOU SO MUCH FOR JOINING US. HAPPY TO BE HERE. OKAY. SO EXPLAIN A LITTLE BIT OF THE PURPOSE OF THIS LAW AND WHO EXACTLY IT’S GOING TO BE AFFECTING. SO THE PURPOSE WAS TO ELIMINATE THE GOVERNMENT PENSION OFFSET AND THE WINDFALL ELIMINATION PROVISION. SO THOSE ARE TWO PROVISIONS OF THE SOCIAL SECURITY ADMINISTRATION THAT BASICALLY SAY IF YOU WERE PAYING INTO SOCIAL SECURITY, EVEN IF YOU PAID IN THE FULL TEN YEARS TO MAKE YOU AN ELIGIBLE EMPLOYEE, AND THEN YOU GOT ANOTHER JOB IN THE PUBLIC SECTOR AS A TEACHER, AS A FIREFIGHTER, AS A POLICE OFFICER, SORRY, YOU’RE GETTING A PENSION FROM THOSE GUYS, AND YOU’RE NOT GOING TO GET AS MUCH OF A PENSION FROM SOCIAL SECURITY, REGARDLESS OF HOW MUCH YOU PUT IN. SO THIS GETS RID OF THAT LAW AND BASICALLY SAYS, HEY, YOU PAID INTO THIS TIME, YOU AT LEAST GET THE AMOUNT THAT YOU WOULD HAVE EARNED OTHERWISE IN THE FORM OF SOCIAL SECURITY, IN ADDITION TO WHATEVER IT IS YOU EARNED FOR, FOR YOUR GOVERNMENT PENSION. SO GOD, IMAGINE THAT THIS WILL NOT COME WITHOUT SOME COST, JUST A LITTLE BIT. UNFORTUNATELY, IT’S NOT WRITTEN INTO THE LAW SPECIFICALLY HOW WE’RE GOING TO PAY FOR IT. SO, YOU KNOW, A LITTLE BIT OF A BLANK CHECK. GOVERNMENT ESTIMATES RIGHT NOW ARE THAT THIS IS GOING TO COST ADDITIONAL COSTS OF $200 BILLION OVER THE NEXT TEN YEARS, BECAUSE THERE’S MORE MONEY BEING PAID OUT BY SOCIAL SECURITY ADMINISTRATION. BY THE WAY, WE’RE ON THE WAY TO INSOLVENCY. SO THIS ADDS ANOTHER HALF A YEAR TOWARD CLOSER TOWARDS INSOLVENCY FOR SOCIAL SECURITY ADMINISTRATION. SO DEFINITELY SOME SOME IMPORTANT DECISIONS TO BE MADE IN THE NOT TOO DISTANT FUTURE. ON THE OVERALL PROGRAM. BUT NOT GOOD RIGHT. GOOD GOOD BUT NOT GOOD. DEPENDING ON YOUR PERSPECTIVE. YEAH, ABSOLUTELY. AND A LOT OF THAT PERSPECTIVE, A LOT OF THAT PERSPECTIVE REALLY DEPENDS ON TIMELINE. SO WHEN DOES THIS TAKE EFFECT. AND WHAT SHOULD BENEFICIARIES KEEP IN MIND AS WELL. SO IF YOU PAID INTO SOCIAL SECURITY FOR AT LEAST TEN YEARS THAT MAKES YOU AN ELIGIBLE RECIPIENT OF SOCIAL SECURITY, REGARDLESS OF WHETHER OR NOT YOU WENT OUT AND GOT A PUBLIC SECTOR JOB. IF YOU ARE ELIGIBLE AND ARE CURRENTLY RECEIVING SOCIAL SECURITY, THE ADMINISTRATION, THE SOCIAL SECURITY ADMINISTRATION ACTUALLY ALREADY KNOWS THAT YOU WERE SUBJECT TO THE WINDFALL ELIMINATION PROVISION AND THE GPO, THE GOVERNMENT PENSION OFFSET THAT WILL ACTUALLY KICK IN AUTOMATICALLY. THE INCREASED BENEFIT SHOULD KICK IN AUTOMATICALLY BECAUSE THEY ALREADY HAVE THAT INFORMATION. OKAY. SO ONCE THEY ACTUALLY START DOING THIS, THE LAW SAYS THAT THEY HAVE TO START PAYING AN ADDITIONAL AMOUNT OUT BY DECEMBER OF THIS YEAR. OH OKAY. SO THERE IS A LITTLE BIT DATES TO JANUARY OF 2024. SO YOU COULD GET A BIG CHUNK OF MONEY. OKAY. SO IT COULD BE A LUMP SUM. OKAY. TO BACK INTO THAT NUMBER. UNFORTUNATELY THE SOCIAL SECURITY ADMINISTRATION IS STILL READING THE LAW AND TRYING TO FIGURE OUT HOW TO IMPLEMENT IT. SO THERE’S NO SPECIFIC TIMELINE ON WHEN THAT’S COMING OUT. ALL RIGHT. SO DO A FAVOR AND MAYBE KEEP US POSTED ON WHEN THAT CHANGE STARTS TO HAPPEN. BECAUSE AGAIN, SOUNDS PRETTY FLUID AS WELL. AB
Preparing retirees for changes in Social Security Fairness Act
Updated: 4:35 PM PST Jan 22, 2025
The Social Security Fairness Act of 2024 promises relief for nearly 3 million former public service employees impacted by reduced or eliminated Social Security benefits due to the WEP (Windfall Elimination Provision) and GPO (Government Pension Offset) rules. The law, signed recently, aims to revoke these provisions, thereby benefiting retirees such as teachers, firefighters, and police officers. Kathryn McCall, a CFP and financial adviser with CAPTRUST, highlights how individuals previously affected due to shifting from private to public sector roles will now receive fair compensation without pension penalties.”The classic example is someone who works for the first 10 or 15 years of their career in the private sector (paying into Social Security) and then decides to become a teacher or firefighter and no longer pay into the program,” McCall said.”Those folks used to be subject to the government pension offset which would aggressively count their government pensions against the amount that they paid into and were eligible to receive as Social Security benefits.”Critically, the legislation lacks a clear financial plan, sparking concern among critics who project an increase of $200 billion in federal debt over the next decade. This could further hasten Social Security insolvency by six months.Regarding implementation, the Social Security Administration is reviewing procedures, with payments backdated to January 2024, and monthly payments expected to rise by December 2025. Beneficiaries need not take any action, as adjustments will occur automatically.See more coverage of top California stories here | Subscribe to our morning newsletter
The Social Security Fairness Act of 2024 promises relief for nearly 3 million former public service employees impacted by reduced or eliminated Social Security benefits due to the WEP (Windfall Elimination Provision) and GPO (Government Pension Offset) rules.
The law, signed recently, aims to revoke these provisions, thereby benefiting retirees such as teachers, firefighters, and police officers.
Kathryn McCall, a CFP and financial adviser with CAPTRUST, highlights how individuals previously affected due to shifting from private to public sector roles will now receive fair compensation without pension penalties.
“The classic example is someone who works for the first 10 or 15 years of their career in the private sector (paying into Social Security) and then decides to become a teacher or firefighter and no longer pay into the program,” McCall said.
“Those folks used to be subject to the government pension offset which would aggressively count their government pensions against the amount that they paid into and were eligible to receive as Social Security benefits.”
Critically, the legislation lacks a clear financial plan, sparking concern among critics who project an increase of $200 billion in federal debt over the next decade. This could further hasten Social Security insolvency by six months.
Regarding implementation, the Social Security Administration is reviewing procedures, with payments backdated to January 2024, and monthly payments expected to rise by December 2025. Beneficiaries need not take any action, as adjustments will occur automatically.
See more coverage of top California stories here | Subscribe to our morning newsletter
The Social Security Fairness Act is a proposed piece of legislation that aims to address some of the inequities and shortcomings in the current Social Security system. For retirees, this could mean significant changes to how their benefits are calculated, distributed, and taxed.As retirees, it’s important to stay informed about these potential changes and how they could impact your retirement income. Here are some steps you can take to prepare for the Social Security Fairness Act:
1. Stay informed: Keep up to date on the latest news and developments regarding the Social Security Fairness Act. This will help you understand how the proposed changes could affect your benefits.
2. Consult a financial advisor: If you have concerns about how the Social Security Fairness Act may impact your retirement income, consider speaking with a financial advisor. They can help you assess your current financial situation and make any necessary adjustments to your retirement plan.
3. Consider alternative sources of income: In light of potential changes to Social Security benefits, it may be wise to explore other sources of income for retirement. This could include investing in a 401(k) or IRA, starting a side business, or exploring part-time employment opportunities.
4. Advocate for fair treatment: If you feel strongly about the proposed changes in the Social Security Fairness Act, consider reaching out to your representatives in Congress to voice your concerns. Your input could help shape the final version of the legislation and ensure that retirees are treated fairly.
By taking proactive steps to prepare for potential changes in the Social Security Fairness Act, retirees can help safeguard their retirement income and ensure a secure financial future. Stay informed, consult with a financial advisor, explore alternative income sources, and advocate for fair treatment to protect your retirement benefits.
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- Social Security Fairness Act
- Retirement planning
- Social Security changes
- Retirement benefits
- Social Security reform
- Retirement income
- Social Security updates
- Social Security fairness
- Retiree financial planning
- Social Security legislation.
#Preparing #retirees #Social #Security #Fairness #Act
New Social Security Fairness Act benefits retired railroad workers
If you’re a retired railroad worker or the spouse or survivor of one, several important changes are coming to your pensions.
Previously, the U.S. Railroad Retirement Board (RRB) reduced the tier-1 amount of an individual’s railroad retirement annuity because of his or her pension.
Now, more money is going back into the pockets of many SMART-TD members.
Signed on January 5 by President Biden, the Social Security Fairness Act ends two statutory reductions (the non-covered service pension (NCSP) and the public service pension (PSP)) that are now known as the windfall elimination provision (WEP) and the government pension offset (GPO).
When the NCSP reduction and PSP offset were enacted over 40 years ago, the initial goal was to equalize social security benefit formulas for workers and their spouses with similar earnings histories.
Calculated based on someone’s lifetime earnings, the NCSP reduction lowered the individual’s primary insurance amount, which ultimately determined the monthly tier-1-annuity amount.
The PSP offset further reduced the tier-1 annuity for spouses or survivors by two-thirds of the gross public pension amount payable to those who fall under either category.
Local government employees with less than 30 years of coverage who also receive a public service pension for work not covered by social security and some federal employees hired before December 31, 1983, are some of the individuals who were affected by the NCSP reduction.
Railroad spouses and widow(er)s who were not covered by social security during the last 60 months of employment with the pension-paying government entity but received a public pension based on their own earnings fall under the category of those who were affected by the PSP reduction.
Thanks to the legislation and the repeal of both provisions, anyone who was impacted by the NCSP reduction and PSP offset will have their full tier-1 benefit amounts restored for months retroactive to December 2023 and future monthly benefit payments.
However, it’s important to note that railroad spouses and widow(er)s whose public employment was covered by social security (including federal employees hired after December 31, 1983) and those who are receiving a public pension not based on their own earnings, were not impacted by the repeal. The Social Security Fairness Act also does not impact existing laws requiring offset of railroad retirement annuities for any social security benefit, public disability benefit, or workers’ compensation received. That means that if you’re receiving an RRB and social security benefit, your tier-1 amount will continue to be offset by the social security benefit.
Click to monitor progress on the implementation of the new law. â–º
Members do not need to take specific action, except for those who have moved or changed banking information. If that applies to your situation, you are asked to report these details to the RRB’s toll-free number by calling (877) 772-5772.
The newly passed Social Security Fairness Act has brought a wave of positive changes for retired railroad workers. This act aims to provide fair and equitable benefits for those who have dedicated their lives to working on the rails.One of the key benefits of this act is the elimination of the Windfall Elimination Provision (WEP) and Government Pension Offset (GPO) for retired railroad workers. These provisions often reduced the Social Security benefits of retired railroad workers, causing financial strain and unfairness in retirement.
With the repeal of these provisions, retired railroad workers can now receive their full Social Security benefits without any reductions. This will provide much-needed financial security for these hardworking individuals who have contributed to the nation’s railway system.
Additionally, the Social Security Fairness Act includes provisions for cost-of-living adjustments (COLAs) to ensure that retired railroad workers’ benefits keep pace with inflation. This will help retirees maintain their standard of living and meet the rising costs of healthcare, housing, and other necessities.
Overall, the Social Security Fairness Act is a significant victory for retired railroad workers, ensuring that they receive the benefits they deserve after years of service on the rails. This act represents a step towards greater fairness and equity in the retirement system for all workers, including those in the railroad industry.
Tags:
- Social Security Fairness Act
- Railroad workers
- Retirement benefits
- Social Security benefits
- Railroad retirement
- Pension benefits
- Social Security Fairness Act benefits
- Railroad worker benefits
- Retired workers
- Railroad retirement benefits.
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Fair HANDSOME 3x60g OFFICIAL USA Deep Action Fairness Whitening Cream Fresh NEW
Fair HANDSOME 3x60g OFFICIAL USA Deep Action Fairness Whitening Cream Fresh NEW
Price :39.98– 24.98
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From Bias to Fairness: Addressing Ethical Concerns in AI Platform and Model Design
Artificial Intelligence (AI) has become an integral part of our daily lives, from powering virtual assistants like Siri and Alexa to driving autonomous vehicles. However, as AI technology continues to advance, there are growing concerns about bias and ethical issues in AI platform and model design.Bias in AI systems can have serious consequences, perpetuating racial and gender stereotypes, reinforcing discrimination, and limiting opportunities for marginalized communities. This bias can be unintentional, stemming from the data used to train AI models or the algorithms themselves. For example, if a facial recognition system is trained predominantly on images of white faces, it may struggle to accurately identify faces of people of color.
To address these ethical concerns, researchers and developers are working to create more fair and unbiased AI systems. One approach is to carefully curate and diversify the training data used to develop AI models. By including a more diverse range of images, texts, and other data, developers can help ensure that AI systems are able to accurately recognize and represent all individuals, regardless of race, gender, or other characteristics.
Another important step is to increase transparency in AI model design, making it easier for researchers and policymakers to understand how decisions are made by AI systems. This includes documenting the data sources used, the algorithms employed, and the potential biases that may exist in the system. By providing this information, developers can help ensure that AI systems are accountable and fair.
In addition to transparency, developers are also exploring ways to incorporate fairness into the design of AI models. This includes developing algorithms that prioritize fairness and equity, such as by minimizing the impact of bias on decision-making processes. By incorporating fairness into the design of AI systems from the outset, developers can help ensure that these technologies are more ethical and just.
Ultimately, addressing bias and ethical concerns in AI platform and model design requires a collaborative effort from developers, researchers, policymakers, and the broader community. By working together to create more fair and transparent AI systems, we can help ensure that these technologies are used in ways that benefit society as a whole. Only by prioritizing fairness and ethics in AI design can we create a more just and equitable future for all.
#Bias #Fairness #Addressing #Ethical #Concerns #Platform #Model #Design,platform and model design for responsible ai
Ensuring Fairness and Accountability in AI: Strategies for Platform and Model Design
Artificial Intelligence (AI) has become an integral part of our daily lives, powering everything from search engines to social media platforms. However, as AI technology continues to advance, concerns about fairness and accountability have come to the forefront. Ensuring that AI systems are fair and accountable is crucial to building trust with users and ensuring that the technology benefits society as a whole.One key aspect of ensuring fairness and accountability in AI is through thoughtful platform and model design. By implementing strategies that prioritize fairness and transparency, developers can create AI systems that are more ethical and responsible. Here are some strategies for ensuring fairness and accountability in AI platform and model design:
1. Diverse and Representative Data: One of the biggest challenges in AI is bias in data. If the training data used to build AI models is not diverse and representative, the resulting AI systems can perpetuate bias and discrimination. Developers should ensure that their training data is inclusive and representative of all demographics to minimize bias in AI systems.
2. Transparency: Transparency is key to ensuring accountability in AI. Developers should strive to make their AI systems transparent by documenting the data sources, algorithms, and decision-making processes used in the model. This transparency can help users understand how AI systems work and hold developers accountable for any biases or errors.
3. Fairness Metrics: Developers should implement fairness metrics to evaluate the performance of AI models across different demographic groups. By measuring fairness, developers can identify and address biases in AI systems before they are deployed in the real world. Fairness metrics can help ensure that AI systems treat all users fairly and equally.
4. Human Oversight: While AI systems can automate many tasks, human oversight is still crucial for ensuring fairness and accountability. Developers should implement mechanisms for human oversight, such as audit trails and feedback loops, to monitor the performance of AI systems and intervene when biases or errors are detected.
5. Ethical Guidelines: Developers should adhere to ethical guidelines and principles when designing AI platforms and models. By following ethical guidelines, developers can ensure that their AI systems respect user privacy, autonomy, and dignity. Ethical guidelines can help developers make responsible decisions when designing AI systems that impact society.
In conclusion, ensuring fairness and accountability in AI is essential for building trust and ensuring that AI technology benefits society. By implementing strategies such as diverse and representative data, transparency, fairness metrics, human oversight, and ethical guidelines, developers can create AI systems that are more ethical, responsible, and trustworthy. Ultimately, by prioritizing fairness and accountability in AI platform and model design, developers can help ensure that AI technology benefits everyone and contributes to a more equitable future.
#Ensuring #Fairness #Accountability #Strategies #Platform #Model #Design,platform and model design for responsible ai
Ethics in Artificial Intelligence: Bias, Fairness and Beyond by Animesh Mukherje
Ethics in Artificial Intelligence: Bias, Fairness and Beyond by Animesh Mukherje
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Ethics in Artificial Intelligence: Bias, Fairness and BeyondArtificial Intelligence (AI) has rapidly advanced in recent years, revolutionizing various industries and transforming the way we live and work. However, with this rapid advancement comes the need to address ethical concerns surrounding AI, especially in terms of bias, fairness, and beyond.
One of the key issues in AI ethics is bias. AI systems are trained on vast amounts of data, which can often reflect historical biases and prejudices. This can result in AI systems making biased decisions, perpetuating existing inequalities and discrimination. It is crucial for developers to actively address and mitigate bias in AI systems to ensure fair and equitable outcomes.
Fairness is another important aspect of AI ethics. AI systems should be designed and deployed in a way that ensures fairness for all individuals, regardless of their race, gender, or other characteristics. This involves considering the impact of AI systems on different groups and ensuring that they do not disproportionately harm or benefit any particular group.
Beyond bias and fairness, AI ethics also encompasses a range of other ethical considerations, such as accountability, transparency, and privacy. Developers must be transparent about how AI systems make decisions and be accountable for their actions. They must also prioritize user privacy and data protection to ensure that AI systems do not infringe on individuals’ rights.
In conclusion, ethics in artificial intelligence is a complex and multifaceted issue that requires careful consideration and thoughtful action. By addressing bias, promoting fairness, and upholding ethical principles, we can ensure that AI technologies benefit society as a whole and contribute to a more just and equitable future.
#Ethics #Artificial #Intelligence #Bias #Fairness #Animesh #Mukherje
Advances in Bias and Fairness in Information Retrieval: 4th International Worksh
Advances in Bias and Fairness in Information Retrieval: 4th International Worksh
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opportunity to discuss and explore the latest advancements in bias and fairness in information retrieval at the upcoming 4th International Workshop.As technology continues to evolve and play a prominent role in our daily lives, it is essential that we address and mitigate biases that may be present in information retrieval systems. These biases can have serious implications, including perpetuating stereotypes, discrimination, and inequality.
The workshop will bring together researchers, practitioners, and experts in the field to share their cutting-edge research, best practices, and insights on how to ensure fairness and mitigate bias in information retrieval systems. Topics that will be covered include algorithmic fairness, bias detection and measurement, ethical considerations, and more.
We invite all those interested in advancing the field of bias and fairness in information retrieval to join us at the workshop. Together, we can work towards creating more inclusive and equitable information retrieval systems for all.
#Advances #Bias #Fairness #Information #Retrieval #4th #International #Worksh
Overcoming Bias and Fairness Issues in Deep Neural Networks
Deep neural networks have revolutionized the field of artificial intelligence and machine learning, achieving remarkable results in a wide range of tasks such as image recognition, natural language processing, and speech recognition. However, one of the challenges that researchers and practitioners face when working with deep neural networks is the issue of bias and fairness.Bias in deep neural networks refers to the systematic errors or inaccuracies in the model’s predictions that are influenced by factors such as the training data, the algorithm used, or the assumptions made during model development. This bias can lead to unfair outcomes, such as discriminatory decisions in hiring practices, loan approvals, or criminal justice systems.
To address bias and fairness issues in deep neural networks, researchers have been exploring various techniques and approaches. One of the key strategies is to ensure that the training data used to train the model is diverse, representative, and free from any biases. This can be achieved by collecting data from a wide range of sources, balancing the representation of different groups in the dataset, and carefully curating the data to remove any biased or discriminatory information.
Another approach to overcoming bias in deep neural networks is to use fairness-aware algorithms that explicitly incorporate fairness constraints into the model optimization process. These algorithms aim to minimize discrimination and promote fairness by penalizing the model for making biased predictions or by adjusting the decision boundaries to ensure equal treatment of different groups.
In addition to data collection and algorithmic approaches, researchers are also exploring the use of interpretability techniques to understand and mitigate bias in deep neural networks. By analyzing the model’s predictions and identifying the factors that contribute to biased outcomes, researchers can make informed decisions about how to improve the model’s fairness and mitigate the impact of bias on the final predictions.
Overall, overcoming bias and fairness issues in deep neural networks is a complex and challenging task that requires a multi-faceted approach. By addressing bias at every stage of the model development process, from data collection to algorithm design to model interpretation, researchers can build more fair and unbiased deep neural networks that can be deployed in a wide range of applications with confidence and trust.
#Overcoming #Bias #Fairness #Issues #Deep #Neural #Networks,dnn
