The Influence Maximization (IM) problem aims to select a set of seed nodes within a given budget to maximize the spread of influence in a social network. However, real-world social networks have several structural inequalities, such as dominant majority groups and underrepresented minority groups. If these inequalities are not considered while designing IM algorithms, the outcomes might be biased, disproportionately benefiting majority groups while marginalizing minorities. In this work, we address this gap by designing a fairness-aware IM method using Reinforcement Learning (RL) that ensures equitable influence outreach across all communities, regardless of protected attributes. Fairness is incorporated using a maximin fairness objective, which prioritizes improving the outreach of the least-influenced group, pushing the solution toward an equitable influence distribution. We propose a novel fairness-aware deep RL method, called DQ4FairIM, that maximizes the expected number of influenced nodes by learning an RL policy. The learnt policy ensures that minority groups formulate the IM problem as a Markov Decision Process (MDP) and use deep Q-learning, combined with the Structure2Vec network embedding, earning together with Structure2Vec network embedding to solve the MDP. We perform extensive experiments on synthetic benchmarks and real-world networks to compare our method with fairness-agnostic and fairness-aware baselines. The results show that our method achieves a higher level of fairness while maintaining a better fairness-performance trade-off than baselines. Additionally, our approach learns effective seeding policies that generalize across problem instances without retraining, such as varying the network size or the number of seed nodes.

The problem of SIM was first introduced in 2003 in Kempe et al.

Many incarcerated individuals face significant and complex challenges, including mental illness, substance dependence, and homelessness, yet jails and prisons are often poorly equipped to address these needs. With little support from the existing criminal justice system, these needs can remain untreated and worsen, often leading to further offenses and a cycle of incarceration with adverse outcomes both for the individual and for public safety, with particularly large impacts on communities of color that continue to widen the already extensive racial disparities in criminal justice outcomes. Responding to these failures, a growing number of criminal justice stakeholders are seeking to break this cycle through innovative approaches such as community-driven and alternative approaches to policing, mentoring, community building, restorative justice, pretrial diversion, holistic defense, and social service connections. Here we report on a collaboration between Johnson County, Kansas, and Carnegie Mellon University to perform targeted, proactive mental health outreach in an effort to reduce reincarceration rates. This paper describes the data used, our predictive modeling approach and results, as well as the design and analysis of a field trial conducted to confirm our model's predictive power, evaluate the impact of this targeted outreach, and understand at what level of reincarceration risk outreach might be most effective. Through this trial, we find that our model is highly predictive of new jail bookings, with more than half of individuals in the trial's highest-risk group returning to jail in the following year. Outreach was most effective among these highest-risk individuals, with impacts on mental health utilization, EMS dispatches, and criminal justice involvement.

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Understanding how robots plan and execute tasks is crucial in today's world, where they are becoming more prevalent in our daily lives. However, teaching non-experts the complexities of robot planning can be challenging. This work presents an open-source platform that simplifies the process using a visual interface that completely abstracts the complex internals of hierarchical planning that robots use for performing task and motion planning. Using the principles developed in the field of explainable AI, this intuitive platform enables users to create plans for robots to complete tasks, and provides helpful hints and natural language explanations for errors. The platform also has a built-in simulator to demonstrate how robots execute submitted plans. This platform's efficacy was tested in a user study on university students with little to no computer science background. Our results show that this platform is highly effective in teaching novice users the intuitions of robot task planning.

Over the previous three years, 3AI have conceptualized and executed 450 pathbreaking & pioneering events, summits, conferences, and speaking interventions with innovative formats, & session tracks to bring out the next-in-class themes & topics in AI & Analytics arena. Through our bespoke, differentiated and curated speaking engagements; our 575 marquee, top-of-line AI & Analytics thought leaders in 3AI TLC (Thought Leaders Circle) representing 490 organizations have shared immense nuggets of topical knowledge & insights with our 23000 active & growing members from working professionals & students community. With 90 partner enterprises and deeply entrenched outreach with 890 organizations, 3AI have assiduously strived to fill up the much-needed void in thought leadership for existing & aspiring AI, Analytics & data science leaders and enhance community build-up, talent outreach, branding & marketing interventions for enterprises, GCCs, IT, BPM, Consulting, Technology & Cloud players, Platform providers & pure-play analytics firms.

After setting out to examine digital healthcare from the inside by launching its own women's health clinic as an app last year, French startup Nabla is executing the next step in a planned pivot to b2b -- announcing today that it's opened its machine learning tech stack to other digital health businesses and healthcare providers so they can offer what it bills as "personalized medicine". Nabla's AI-powered patient communications and engagement/retention platform is designed to support clinicians to deliver a more continuous, data-driven service, whether the client is offering real-time telehealth consultations or delivering a service to patients via asynchronous, text-based messaging. Nabla's messaging and teleconsultation communication modules sit as a layer atop the customer healthcare service, ingesting and structuring patient data -- with its machine learning software supporting clinicians with real-time prompts and visualizations, as well as offering ongoing patient outreach features to extend service provision. The startup argues its approach can improve medical outcomes by supporting healthcare professionals to be able to ask relevant questions during a consultation, based on the AI's ability to aggregate patient activity and surface contextually relevant data -- and afterwards, with features like automated transcription and by suggesting updates a clinician could make to a patient's medical file. It likens the platform's capabilities to having a really attentive family doctor who knows their patient's full medical history and situation -- and has a fault-less memory for all that detail.

I am the Chief Operating Officer and co-founder of CallTrackingMetrics and work closely with customers across a variety of industries helping them translate conversations into impact and drive conversions. Prior to CallTrackingMetrics, I was working in management consulting –– managing commercial teams and traveling every week. But, after I had my first child, I resigned and wanted to regroup. A few years later, my husband, Todd and I started CallTrackingMetrics in our basement. Todd had a day job at that point, so we began working on CTM at night and did that for about two years.

Recruiting is a top concern for enterprises in 2021. In a survey by XpertHR, roughly one-half of responding employers plan to increase their workforce in 2021, but expect that hurdles will stand in the way. A high volume of low-quality applicants is stymying the search for the ideal candidates, with one source pegging the average number of unqualified applicants at 75%. Even among those that do make it through the recruiting funnel, a significant portion ultimately change their minds -- exacerbating the recruiting challenge. Against this backdrop, Sense, an "AI-driven" talent engagement and communications platform, today announced that it raised $50 million in series D funding led by SoftBank.