We made #WVH so that AI could learn from your culture.  

We get the irony of using 'Superman' as an example of bias. 

Superman's creators, Joe Shuster and Jerry Siegel, created a character  who defined strength & purity in the terms that suited 1930's America, is your AI doing much differently in hiring today? 

#WVH believes you need to nurture every single piece of fruit. 

"#WholeVillageHiring™ uses the power of shared experience to build Inclusion between the talent already inside your company and each talent it is engaging. The choice of which content conveys what aspect of your company and the wider life around the location of your jobs, is the ongoing nurturing of fruit. Managing this is the work referred to above by Mo Gawdat. Hence why #WVH operates the opposite of the large language model concept, in short language models, defined and centred around each client company. 
The ethics of our central design and the impact of centralised updates that could impact a local SLM are monitored by #WVH being the first AI model in the world to have ongoing, unfettered external academic supervision. We do not mark our own homework.   
The AI assistance that #WVH seeks to develop and co-parent with its clients is called - Quinn™ from the Irish - ‘O'Cuinn’ which means: “Chief/Counsel/Wisdom”

When it comes to AI you can't mark your own homework 

What does it mean to have an AI model that gives ongoing, unfettered external academic supervision. We do not mark our own homework? 
A large company creating AI in hiring designed, launched and supported a product that used the analysis of facial expressions as part of automating a general AI question that #WVH does not seek to address: "Should this person be selected for this job / interview" - #WVH resolves "Can we get this person to join the process?"
Due to a compliant made by the Electronic Privacy Information Center to the Federal Trade Commission alleging the company undertook an algorithmic audit by O’Neil Risk Consulting & Algorithmic Auditing (ORCAA) on the role of audio vs visual features’ in evaluating job candidates. The company has since stopped using visual analysis. We seek a model that prevents harm, not to escalate the ill advised use of AI for retrospective remedy. Not a model of fixing stuff, more do no harm. To value the limits of what you should do, is often less than what you could do. 

Early days for small language models and AI at the edge

Small, or smaller, language models are more cost-effective to deploy than LLMs, and offer greater privacy and – potentially – security. While LLMs have become popular due to their wide range of capacities, SLMs can perform better than LLMs, at least for specific or tightly defined tasks.
SLMs can be trained on private, and often sensitive, data. Even where data is not confidential, using an SLM with a tailored data source avoids some of the errors, or hallucinations, which can affect even the best LLMs.
With smaller language models, the option to run on local hardware brings a measure of cost control. The up-front costs are capital expenditure, development and training. But once the model is built, there should not be significant cost increases due to usage. “For a small language model, they have been designed to absorb and learn from a certain area of knowledge,” says Jith M, CTO at technology consulting firm Hexaware

#WVH wants you to spend less money, not more

#WVH is working with a select group of 'venture clients' that will enable us to develop our enterprise pricing model which will be 100% focused on returning the majority of existing external service spend back to your own organisation. 

Your organisation is yet to start supplier diversity in your region?

Our easy 1+2+3 process.