Maximising crowd based efficiency through advanced automation and system rules with you guessed it..
AI
Finally Introducing AI to Crowd-Sourcing
AI represents a transformative force within the realm of crowdsourcing. At Poola, we embrace this cutting-edge technology to streamline and augment essential processes, ensuring that every contributor's journey is marked by efficiency and satisfaction. By harnessing the power of AI, we optimise task allocation, facilitating a seamless match between contributors and the Pools that align with their skills, resources and passions. Our commitment to AI extends to quality control, as AI-driven algorithms aid in content moderation, guaranteeing that all contributions adhere to community guidelines.
Through personalised recommendations, users are effortlessly guided toward projects that resonate with their interests and previous contributions. AI's robust capabilities extend to fraud detection, fortifying the integrity of our platform. With AI as our ally, Poola remains dynamic and adaptive, readily embracing new challenges and opportunities within the ever-evolving landscape of crowdsourcing. As we advance, AI remains at the heart of our commitment to deliver a more efficient, reliable, and valuable experience to those who choose Poola as their platform of choice for making a difference in the world.
01
Automated Matching
At Poola we offer 'alternative contribution abilities' where users can contribute their skills, time and physical resources to Pools. With us users can save these 'skills, abilities, available time, and physical resources' and AI can match them with the most relevant Pools, ensuring a better fit for participants and improving project success rates.
02
First Responder Pools
Poola can leverage the power of AI and strategic partnerships with reputable news organisations to respond swiftly to major global news events. Our aim is to establish social good pools promptly when significant news stories break worldwide. This process involves harnessing AI technology to analyse trends in charitable web traffic, identifying areas where immediate support is needed.
03
Quality control and review
AI plays a pivotal role in content moderation, enabling us to maintain a safe and compliant environment. Through machine learning, we can automatically monitor user-generated content, swiftly identify violations, and uphold community guidelines, minimising the need for manual intervention and time waste.
04
Fraud Detection and Security
Our algorithms can be employed to assess and validate the quality of our contributions. Through natural language processing and machine learning techniques, AI can analyse the accuracy, relevance, and coherence of textual inputs, helping to filter out irrelevant or low-quality contributions. This ensures that only high-quality outputs are accepted, improving the overall reliability and credibility of Poolas platform.
05
Personalised Recommendations
AI-driven recommendation engines can be designed to provide users with tailored suggestions based on their past contributions and preferences. This personalisation enhances user engagement, making it easier for participants to discover and engage with the Pools that resonate with them.
06
Predictive Analytics
AI can analyse historical data to predict which Pools are more likely to succeed or require additional support, enabling more informed decision-making by both contributors and project organisers. We can now better take into account regional and cultural differences to provide nuanced insights.
07
Natural Language Processing (NLP)
NLP algorithms can help analyse and categorise text-based contributions, making it easier to search for relevant information within a pool and providing valuable insights. We can then accurately AB test and review every new 'alternative contribution ability' we launch.
Poola is putting AI To Good Use
There are a number of issues that are at the forefront of ethical conversations surrounding AI technologies in the real world. We at Poola aim to be responsible and utilise 'common sense' practises in our development and implementation of AI.
Examples of these AI ethics issues include data responsibility and privacy, fairness, explainability, robustness, transparency, environmental sustainability, inclusion, moral agency, value alignment, accountability, trust, and technology misuse.