#AlgorithmicBias

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Artificial Intelligence Ethics Courses - The Next Big Thing?

With increasing integration of artificial intelligence into high stake decisions around financial lending, medical diagnosis, surveillance systems and public policies –calls grow for deeper discussions regarding transparent and fair AI protocols safeguarding consumers, businesses and citizens alike from inadvertent harm. 

Leading technology universities worldwide respond by spearheading dedicated AI ethics courses tackling complex themes around algorithmic bias creeping into automated systems built using narrow data, urgent needs for auditable and explainable predictions, philosophical debates on superintelligence aspirations and moral reasoning mechanisms to build trustworthy AI.  

Covering case studies like controversial facial recognition apps, bias perpetuating in automated recruitment tools, concerns with lethal autonomous weapons – these cutting edge classes deliver philosophical, policy and technical perspectives equipping graduates to develop AI solutions balancing accuracy, ethics and accountability measures holistically. 

Teaching beyond coding – such multidisciplinary immersion into AI ethics via emerging university curriculums globally promises to nurture tech leaders intentionally building prosocial, responsible innovations at scale.

Posted By:

Aditi Borade, 4th year Barch,

Ls Raheja School of architecture 

Disclaimer: The perspectives shared in this blog are not intended to be prescriptive. They should act merely as viewpoints to aid overseas aspirants with helpful guidance. Readers are encouraged to conduct their own research before availing the services of a consultant.

#ai #ethics #university #course #TechUniversities #AlgorithmicBias #AuditableAI #ExplainableAI #Superintelligence #MoralReasoning #TrustworthyAI #CaseStudies #FacialRecognitionEthics #RecruitmentToolsBias #AutonomousWeaponsEthics #PhilosophyTech #PolicyPerspectives #EnvoyOverseas #EthicalCounselling #EnvoyCounselling #EnvoyStudyVisa

[weetechsolution]

TikTok's Experiment with AI-Generated Searches

The AI-driven search results, one of which, according to a recent report, was the smart speaker, looks to be a significant investment in optimization, and one experiment that directly benefits subscribers and minimizes content filtering time. The project transcends a typical approach of human selection and paves the way for an entirely different people-website interaction.

The Experiment

TikTok is planning to utilize machine learning models to predict user preferences and give more relevant search results. TikTok's AI systems, when used by TikToks app developers, can create personalized search results based on the preferences of individual users through the analysis of tons of data that comprises information about user interactions, content choices, and engagement trends.

The Potential Impact

The experiment by TikTok can play a crucial as well as positive role in transforming the experiences of the users particularly when it gains advances. Instead, the app acquires the potential to raise user satisfaction by shortening their sessions through producing a list of search results that are more relevant and specific to the user. Furthermore, this could be the start which would lead to the unprecedented hike in advertising earnings and would drive the TikTok like no other into the highest and strongest position on the market.

Privacy Concerns

TikTok's movement into AI-generated search results not only raises serious privacy concerns but also puts forth potential issues that could result in life worse with the help of clever algorithms. That's amidst some of the mass denial who, opposing the rapid use of AI algorithms in content curation, argue that the problems of algorithmic bias and data privacy can be exacerbated. The company's AI algorithms may support features such as making some software or declining the setting, giving space for possible use and this would lead to them maintaining negative associations with certain groups and situations and even this might make people worry about what they could possibly expect

Regulatory Scrutiny

While regulatory attention over algorithmic transparency and data protection is on the rise, TikTok is experimenting with AI-generated search results. Across the globe, regulators are keeping a close eye on how internet companies are utilizing AI algorithms to ensure adherence to privacy laws and stop false information from spreading. To stay out of legal trouble and preserve its good name, TikTok will need to handle these obstacles with skill.

Conclusion

In a bold attempt to minimize costs and keep one step ahead of competitors, TikTok chose to test AI-generated search results. But the program also brings up some significant issues with algorithmic bias, privacy, and compliance with the law. Transparency, accountability, and user trust should be TikTok's top priorities as it develops its AI algorithms to provide a secure and pleasurable experience. Whether TikTok's bet on AI-generated search results turns out to be profitable or not will only be determined over time.

#TikTokAIGeneratedSearchTest #TikTokTest #AIGeneratedSearchTest #SearchRevolution #AlgorithmicBias #EfficientSearch #SocialExperiment #WhatDoYouThink #SocialMediaTrends #TheFutureIsNow

[happypeaceaman]

Unveiling the Mystery of Dynamic Pricing in Flight Ticket Booking

In the ever-evolving landscape of airline ticket booking, there's a phenomenon that often leaves travelers perplexed and sometimes frustrated: dynamic pricing. If you've ever searched for a flight ticket only to find the price mysteriously fluctuating within minutes, you've experienced dynamic pricing in action. But what exactly is dynamic pricing, and why does it seem to confound passengers? Let's delve into the intricacies of this pricing strategy and uncover the factors driving its implementation in the airline industry.

For more features please visit https://trailtravelz.com/

Understanding Dynamic Pricing

Dynamic pricing, also known as surge pricing or demand pricing, is a strategy wherein businesses adjust the price of their products or services based on various factors such as demand, inventory levels, time of purchase, competitor pricing, and customer demographics. While this strategy is employed across various industries, it has become particularly prevalent in the airline sector.

In the context of flight ticket booking, dynamic pricing means that the cost of airfare can change rapidly and frequently, even within short intervals. This means that two passengers booking the same flight at the same time may end up paying different prices, depending on when they initiate the booking process and a myriad of other factors.

Factors Influencing Dynamic Pricing in Flight Ticket Booking

Demand-Supply Dynamics: One of the primary factors influencing dynamic pricing in flight ticket booking is the interplay between supply (available seats) and demand (number of passengers seeking tickets). When demand is high and supply is limited, airlines may raise prices to capitalize on the situation. Conversely, during periods of low demand or excess capacity, prices may drop to stimulate sales.

Time Until Departure: The time remaining until the departure of a flight is a crucial determinant of pricing. Airlines often employ yield management systems that analyze historical data to predict demand patterns at different times before departure. As the departure date approaches and the flight fills up, ticket prices tend to increase, incentivizing early bookings.

Seasonality and Events: Seasonal trends and major events can significantly impact air travel demand and, consequently, ticket prices. For example, flights to popular tourist destinations may be more expensive during peak travel seasons, such as summer or holidays. Similarly, prices may surge during major events like music festivals, sporting events, or conferences held in specific locations.

Competitor Pricing: Airlines closely monitor the pricing strategies of their competitors and may adjust their own prices accordingly. If a rival carrier reduces fares on a particular route, others may follow suit to remain competitive, leading to dynamic fluctuations in ticket prices across multiple airlines.

Personalized Pricing: With the advent of big data and sophisticated algorithms, airlines can now tailor prices to individual customers based on their browsing history, purchase behavior, loyalty status, and other factors. This personalized pricing approach allows airlines to maximize revenue by charging different prices to different customers for the same seat.

Challenges and Controversies

While dynamic pricing offers benefits such as revenue optimization and flexibility, it also presents challenges and controversies, especially from the perspective of consumers. Some of the common concerns associated with dynamic pricing in flight ticket booking include:

Lack of Transparency: The opacity surrounding dynamic pricing algorithms can leave consumers feeling frustrated and distrustful. Without clear explanations for price fluctuations, passengers may perceive the process as arbitrary or unfair.

Price Discrimination: Personalized pricing practices raise ethical questions regarding price discrimination based on factors such as income level, purchase history, or geographic location. Critics argue that such practices may exacerbate inequalities and disadvantage certain groups of travelers.

Consumer Protection: The dynamic nature of pricing makes it difficult for consumers to predict and budget for travel expenses accurately. This unpredictability can be particularly problematic for budget-conscious travelers or those with inflexible travel plans.

Algorithmic Bias: Concerns have been raised about the potential for algorithmic bias in dynamic pricing systems, whereby certain demographic groups may be systematically disadvantaged or targeted for higher prices based on algorithmic assumptions.

Navigating Dynamic Pricing as a Consumer

While consumers may not have direct control over dynamic pricing practices, there are strategies they can employ to navigate the complexities of airline ticket booking:

Book Early: Whenever possible, book your flights well in advance to secure lower prices before they start to increase closer to the departure date.

Be Flexible: Flexibility with travel dates, times, and destinations can provide opportunities to find better deals, especially during off-peak periods.

Use Incognito Mode: Browsing for flights in private or incognito mode can prevent airlines from tracking your online activity and potentially adjusting prices based on your search history.

Sign Up for Alerts: Subscribe to fare alerts and newsletters from airlines or third-party travel websites to stay informed about price drops and special promotions.

Consider Alternative Airports: Exploring nearby airports or alternative routes may uncover cheaper flight options, particularly for regional or connecting flights.

Conclusion

Dynamic pricing has become a ubiquitous aspect of flight ticket booking, shaping the way airlines manage pricing and revenue. While this pricing strategy offers benefits in terms of revenue optimization and market responsiveness, it also raises concerns regarding transparency, fairness, and consumer protection. As travelers navigate the intricacies of dynamic pricing, understanding the factors driving price fluctuations and adopting savvy booking strategies can help mitigate its impact and secure the best possible deals.

#DynamicPricing #FlightBooking #AirlineTickets #PricingStrategy #TravelIndustry #DemandSupply #PersonalizedPricing #PriceFluctuations #ConsumerProtection #Transparency #AlgorithmicBias

[steveganger]

"Unleashing Intelligence: Navigating the Frontier of Artificial Intelligence"

AI applications have become integral to numerous aspects of our daily lives, from virtual assistants on our smartphones to recommendation algorithms on streaming platforms. In healthcare, AI aids in diagnostics and drug discovery, while in finance, it facilitates fraud detection and risk management. Industries are leveraging AI to optimize processes, enhance productivity, and unlock new possibilities.

#ArtificialIntelligence #MachineLearning #DeepLearning #AIInnovation #TechRevolution #AIEthics #FutureTech #DigitalTransformation #SmartTechnology #AutonomousSystems #DataScience #HumanAIInteraction #InnovationHub #AlgorithmicBias #AIApplications #Robotics #IntelligentSystems #TechEthics #Automation #CognitiveComputing

[omanxl1]

The Holding Pattern (Part Six)

The Holding Pattern (Part Six)

They’re in a holding pattern like being stuck in traffic on I-20 eastbound before you get to Wesley Chapel Road in the Atlanta area.  Meanwhile, O-Zone?  beauty beholding, the pattern is compelling per the saga / struggle after I peeped game  / understand the scenario.  Chilling out in our remote outpost out here off of I-20 In Atlanta; visualizing the sequence! as tomorrow whispers in my ear I…

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#algorithmicbias #atlanta #BidenvsMAGARepublicans #djmixes #EPMD #FabulousFriday #FlashbackFriday #gracejones #HoldingPattern #Louisville #poetry #politics

[manoasha]

"Ethics in Artificial Intelligence: Navigating the Moral Landscape"

In the ever-expanding realm of artificial intelligence (AI), the ethical considerations surrounding its development and deployment have become paramount. As AI technologies continue to shape various aspects of our lives, from healthcare to finance and beyond, it is crucial to embark on a thoughtful exploration of the moral landscape that accompanies these powerful innovations. The Ethical…

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#AIethics EthicalAI AlgorithmicBias PrivacyConcerns AutonomousSystems TransparencyInAI JobDisplacement GlobalAI RegulationAndOversig

[seheiraking]

Algorithm bias is real

#algorithmicbias

[stag28]

"Via supervised machine learning, we build four classifiers (logistic regression, KNN, SVM, CNN) using facial images of 1856 real persons controlled for race, gender, age and facial expressions, nearly half of whom were convicted criminals, for discriminating between criminals and non-criminals. All four classifiers perform consistently well and produce evidence for the validity of automated face-induced inference on criminality"

#predictiveanalytics #facialrecognition #machinelearning #algorithmicbias

[secretcolorthing]

Tweeted

Cumulative biasing is always worse than targeted ones #AlgorithmicBias #AIEthics

— paperback 🍕 (@gaaneshaha) Sep 21, 2021

#IFTTT #Twitter

[surveycircle]

Teilnehmer für Online-Studie gesucht! Thema: "Ethical Consequences of Algorithmic Biases in Marketing" https://t.co/FszqGXfRDf via @SurveyCircle#marketing #OnlineAds #AlgorithmicBias #website #webseitenverhalten #master #umfrage #surveycircle pic.twitter.com/h7PlNNMfgc

— Daily Research @SurveyCircle (@daily_research) June 19, 2020

#finding participants #finding respondents #participants needed #get survey responses #larger sample

[sociologyyouthblaze]

Elijah Falk

Blog entry #6, submitted on Wednesday, March 11, 2020

Concept: Algorithmic bias and social inequality

In her Ted Talk, Joy Buolamwini discusses algorithmic bias, or what she calls the “coded gaze.” She recounts her own personal experiences with algorithmic bias as a black, female computer scientist. Often times, facial recognition software fails to recognize her face while functioning perfectly on others with lighter skin tones. She outlines three main contributing factors to this algorithmic discrimination: who codes, how we code, and why we code. Buolamwini explains that bias in artificial intelligence is a direct result of the data received. The people writing the code for these algorithms are disproportionately white and male, and the data they input often reflects that. She states that an increase in diverse and socially conscious coders, representative and conscientious data, and consideration of ethical implications in tech will help combat algorithmic bias. Like this week’s readings, Buolamwini includes a tangible example of the harm that this bias can cause, highlighting the use of facial recognition technology in policing that increases racial profiling in the criminal legal system. Both articles this week were centered around disparates in health and life chances, which are exacerbated by algorithmic and implicit biases. Just as Buolamwini does in her Ted Talk, Ruha Benjamin demonstrates the ways in which algorithms reflect societal biases and perpetuate inequality under the guise of neutrality. Roberta Spalter-Roth, Terri Ann Lowenthal, and Mercedes Rubio do not focus on artificial intelligence in the same way, but still demonstrate the ways in which a legacy of segregation and discrimination influences a seemingly unbiased system. They acknowledge a myriad of factors that contribute to disparate health outcomes across racial and class lines, stemming from Jim Crow era legislation and practices.

#AlgorithmicBias #CodedGaze #JimCrow #ImplictBias #racism

https://www.youtube.com/watch?v=UG_X_7g63rY

#pzsoc199a #submission